Treatment Adherence and Compliance

Abstract

Treatment adherence and compliance is the broad class of behaviors through which a person carries out, or departs from, a health-care provider's recommendations for prevention, diagnosis, and treatment. It is the umbrella under which medicine groups patient compliance, medication adherence, appointment-keeping, and their refusals, and it names one of the largest problems in health care: averaged across conditions, roughly a quarter of patients do not follow recommendations, and adherence to long-term therapy for chronic disease averages only about half. Because that gap is weakly a matter of knowledge and strongly one of belief, memory, and competing demands enacted over time, adherence belongs to the cognitive psychology of goal pursuit as much as to clinical practice. This article covers how the class is defined and measured, how a regimen fails, the beliefs that predict it, and the modest effect of interventions.

Keywords: patient compliance, medication adherence, necessity-concerns framework, adherence taxonomy, intention-behavior gap

Treatment adherence and compliance is where a clinical recommendation meets the rest of a patient's life. A plan made in a consulting room — take this daily, come back in three months, change this habit — is an instruction issued once for behavior that must be carried out repeatedly over weeks, months, or years, and the distance between issuing that instruction and executing it faithfully is filled with forgetting, doubt about whether the treatment is needed, worry about its harms, cost, side effects, and the ordinary friction of a complicated life. The question that organizes the field is why so many patients who sought care, accepted a diagnosis, and endorsed a plan nonetheless depart from it, and the answer has moved steadily away from blaming the patient's will and toward understanding non-adherence as a predictable product of beliefs, cognition, and circumstance that can be measured and, within limits, reshaped.

What Treatment Adherence and Compliance Is

Treatment adherence and compliance, in the usage adopted by the United States National Library of Medicine, names the behaviors associated with carrying out a health professional's recommendations for prevention, diagnosis, or treatment. It is deliberately broad: introduced as a Medical Subject Heading in 2017 to sit above the older, narrower terms, it gathers under one roof the taking of medicines, the keeping of appointments, the following of diets and lifestyle advice, and the acceptance, satisfaction, interruption, and refusal that surround them. The heading exists because these behaviors share a common structure — a recommendation issued by one party and enacted, over time, by another — and because the determinants of following a diet and of taking a pill turn out to overlap far more than they differ (Kardas et al., 2013).

The vocabulary within this class carries a history. The oldest term, compliance, frames the clinician as the source of a correct instruction and the patient as the party who does or does not obey, and for that reason it drew sustained criticism as implying a one-way relationship that mislocates the cause of non-adherence in patient fault. The preferred modern term, adherence, keeps the same measurable behavior — the match between recommendation and action — while dropping the connotation of obedience; concordance, a later term, refers to the prior negotiation and agreement between prescriber and patient rather than to the behavior itself. The MeSH heading keeps both words precisely to span the shift.

The size of the problem is what gives the terminology its weight. A quantitative review of fifty years of research found that, averaged across conditions and regimens, about a quarter of patients fail to adhere to medical recommendations, with wide variation by disease, by regimen complexity, and by how adherence is measured (DiMatteo, 2004). An influential clinical review put the same point in operational terms: adherence to long-term therapy for chronic illness in developed countries averages only about half, so the problem is not a minority of difficult patients but a routine feature of care that any treatment plan must anticipate (Osterberg & Blaschke, 2005).

Types of Treatment Adherence and Compliance

Within the MeSH hierarchy, Treatment Adherence and Compliance is the parent heading for a set of narrower descriptors, each naming a distinct behavior in the class. MeSH is an indexing vocabulary, not a psychological taxonomy, so these subtypes are neither mutually exclusive nor jointly exhaustive — a single patient's behavior can involve several at once, and the tree reflects how the literature is catalogued rather than a theory of how the behaviors are generated. The direct narrower descriptors are:

- Patient Acceptance of Health Care — the extent to which patients take up and accept the health care and procedures offered to them; the gateway behavior on which the rest depend. - Patient Satisfaction — the patient's evaluation of the care received, a correlate and sometime predictor of whether recommendations are followed. - Treatment Interruption — the discontinuation, planned or unplanned, of a treatment before its intended course is complete. - Treatment Refusal — the patient's declining of recommended care, the explicit negative pole of the class.

Two of the most studied behaviors in the class, patient compliance and medication adherence, sit further down the same branch, beneath Patient Acceptance of Health Care, and are treated in their own articles. The remainder of this article addresses the class as a whole: how it is measured, how it fails, and why the cognitive psychology of goal pursuit explains the pattern better than appeals to the patient's will.

Measuring Adherence and Compliance

No measure of adherence is both cheap and accurate, and the choice of measure systematically shapes the number obtained. Self-report is inexpensive and can reveal a patient's reasons, but it reliably overestimates adherence, because patients forget lapses and are reluctant to disclose them. Pill counts and pharmacy-refill measures such as the proportion of days covered are more objective but record only that a dose was removed or dispensed, not that it was taken. Electronic monitoring — a cap that timestamps each bottle opening — comes closest to the behavior but is costly and still cannot confirm ingestion. The practical consequence is that reported adherence rates depend as much on the instrument as on the patients, and that a measure chosen for a study must be matched to the phase of adherence it is meant to capture (Vrijens et al., 2012) (Kvarnström et al., 2021).

Set a patient's true adherence, then read what each instrument reports. Every lens over-reports, but by a known amount: self-report most, electronic monitoring least. The reported rate depends as much on the instrument as on the patient.

Spread across instruments at this true level: 6 percentage points between the most and least biased lens.

The gap between the dashed truth line and each bar is the instrument's over-report; it shrinks to nothing as true adherence nears 100% and is widest in the middle range, where most patients actually sit. This is why a measured adherence rate must be read together with the method that produced it (Vrijens et al., 2012). Biases are illustrative and direction-faithful, not calibration data.

The Behavioral Cascade

The most useful structural insight in the field is that adherence is not one behavior but a sequence of distinct phases, each of which a patient must complete for a recommendation to translate into a correctly executed treatment. The consensus ABC taxonomy divides the medication-taking case into initiation (taking the first dose at all), implementation (how closely actual dosing matches the prescription over time), and persistence (how long the patient continues before discontinuing), with an acceptance step preceding them in which the patient takes up the plan in the first place (Vrijens et al., 2012). Because a patient must clear every stage, the stages multiply: a regimen that retains 90% at acceptance, 90% at initiation, 80% at implementation, and 70% at persistence leaves well under half fully adhering, since the losses compound rather than average.

Set each phase for a cohort of 1000 patients. Because a patient must clear every stage, the number still fully adhering is the running product of the stages, not their average — so losses compound down the chain.

90% × 90% × 80% × 70% = effective adherence 45.4% — 454 of 1000 fully adhering

At the default 90% / 90% / 80% / 70% the cohort reproduces the Worked Example: 45.4%, or 454 fully adhering. The phase with the most room to improve returns the most — lifting persistence alone from 70% to 90% recovers roughly 129 patients — which is why locating where the cascade fails matters more than a general push to comply (Vrijens et al., 2012). Illustrative values within the ranges reported for long-term therapy.

Locating where in the cascade a regimen fails matters more than exhorting patients to comply in general, because the remedy differs by stage: a failure of initiation is addressed differently from a slow erosion of persistence, and a single reminder that helps implementation does nothing for a patient whose concerns have led them to refuse the plan outright (Kini & Ho, 2018).

Beliefs: Necessity and Concern

Whether a patient follows a treatment is predicted less by what they know than by what they believe about it. The necessity-concerns framework holds that adherence reflects the balance a patient strikes between their perceived necessity of a treatment and their concerns about its potential harms; when concerns come to outweigh necessity, the patient revises the plan, often silently (Horne et al., 2013). A meta-analysis across many illnesses confirmed that higher necessity beliefs and lower concern beliefs each independently predict better adherence, and that the balance between them predicts it across conditions as different as asthma, diabetes, and HIV (Foot et al., 2016). This belief balance sits downstream of broader models of health behavior — the health belief model, in which action follows from weighing perceived threat against the benefits of and barriers to acting (Rosenstock, 1974), and the theory of planned behavior, in which a behavioral intention is formed from attitudes, norms, and perceived control (Ajzen, 1991) — but it is more proximal to the act and so predicts it more tightly.

Sixty-four patients span every combination of perceived necessity (vertical) and concerns(horizontal). A patient adheres when necessity exceeds concerns by at least their caution threshold. Raise the threshold to model more cautious patients and watch the boundary sweep across the population.

36 of 64 adhering, 28 not — split by the dashed belief-balance line.

Adherence follows a diagonal boundary in the belief plane: patients above the line judge the treatment necessary enough to outweigh their concerns, those below do not. Shifting the whole population's information changes little; shifting where the balance tips — by raising felt necessity or easing concerns — moves the line and with it the count (Horne et al., 2013; Foot et al., 2016). An illustrative decision rule, not a fitted model.

Determinants and Consequences

A review of systematic reviews grouped the determinants of adherence into five interacting sets: socioeconomic factors, the health-care team and system, the condition, the therapy, and the patient — a structure that makes clear why non-adherence resists a single remedy, since a cause in any one set can sink an otherwise well-supported regimen (Kardas et al., 2013). Among patient-level causes, the cognitive ones are prominent: forgetting is among the most commonly cited reasons for missed doses, and the demands a complex regimen places on memory and planning compound across every repetition (Zogg et al., 2012). Qualitative work adds that a patient's lived experience of a condition — the felt necessity of treatment, the daily burden it imposes, the trust placed in the prescriber — shapes adherence in ways a checklist of risk factors misses (Kvarnström et al., 2021).

The consequences are large and well documented. Non-adherence worsens clinical outcomes, drives avoidable hospitalization, and wastes a substantial fraction of health expenditure, and the authoritative systematic review of interventions concluded that even the most effective are complex and labour-intensive while their effects remain inconsistent and generally modest (Nieuwlaat et al., 2014). A later meta-analysis of 771 intervention trials reached the same sobering verdict: improvements are real but small, and no single approach dominates (Conn & Ruppar, 2017).

Worked Example

Consider a cohort of 1,000 patients newly prescribed a long-term medicine, and suppose the four phases of the cascade retain patients at the illustrative rates below. Because a patient must clear every phase, the fraction still fully adhering at the end is the product of the per-phase rates, and the number of patients is that product times the starting cohort.

The arithmetic makes two things concrete. First, because the phases multiply, the overall figure is far lower than any single phase suggests: rates of 0.90, 0.90, 0.80, and 0.70 — none alarming on its own — leave 0.90 × 0.90 × 0.80 × 0.70 = 0.454, so fewer than half of the 1,000 patients are fully adhering at the end. Second, the phase with the most room to improve yields the most: lifting persistence from 0.70 to 0.90 raises the final fraction to 0.90 × 0.90 × 0.80 × 0.90 = 0.583, recovering about 129 patients, far more than a comparable gain at a phase that is already high. This is why identifying the phase at which a regimen fails matters more than a generic push toward compliance, and why a single intervention, touching one phase in a chain of four, rarely transforms the headline rate.

Discussion

The study of treatment adherence has followed a clear arc: away from a model that located the fault in the patient's character and toward one that locates the behavior in a web of beliefs, cognitive failures, and practical barriers that systems can measure and partly correct. The early framing treated non-compliance as a failure to obey a correct instruction, a framing that both misdescribed the behavior and predicted it poorly because it looked for the cause in the patient's will rather than in the interaction between the patient's beliefs and the regimen. The broad MeSH heading, uniting compliance and adherence with acceptance, satisfaction, interruption, and refusal, encodes the mature view: these are facets of one behavioral class governed by common determinants (Kardas et al., 2013) (DiMatteo, 2004).

What follows from this relocation is a strategy of understanding and targeting rather than exhortation. Because the determinants are multiple and interacting, and because patients act on their own beliefs about necessity and risk, the productive response is to identify the specific barrier a given patient faces — a belief, a phase of the cascade, a practical obstacle — and address that, rather than to apply a generic push toward compliance (Horne et al., 2013). The gap between the clarity of this diagnosis and the modest size of the available remedies is the field's defining tension: the causes of non-adherence are now well understood, yet improving it remains slow, complex work, because there is no single lever when there is no single cause (Nieuwlaat et al., 2014) (Conn & Ruppar, 2017).

Cognitive Implications

Non-adherence is, at its psychological core, an instance of the intention-behavior gap operating repeatedly over time. A patient who accepts a treatment has formed an intention to carry it out, yet a formed intention is a weak predictor of the eventual act, and the weakness is compounded for a long-term regimen because the intention must be re-enacted on schedule for months or years, surviving forgetting, doubt, and competing demands at each repetition (Ajzen, 1991). This is why non-adherence is so weakly a knowledge problem: the patient rarely lacks the information or the initial intention but fails to convert it into the recurring act. The necessity-concerns framework describes the belief side of this gap — a patient whose concerns come to outweigh perceived necessity revises the intention itself (Horne et al., 2013) — while prospective-memory demands describe the execution side, since every scheduled dose is a cue-dependent future task that must be remembered at the right moment (Zogg et al., 2012).

The most directly applicable cognitive remedies address exactly these two failure modes. Forming an implementation intention — an explicit if-then plan that links a treatment behavior to an existing daily routine, a specific meal or the act of brushing teeth — delegates its initiation to an anticipated situational cue and so protects the implementation and persistence phases where everyday adherence erodes (Gollwitzer, 1999). Strengthening a necessity belief or allaying a specific concern addresses the belief side before it revises the intention. Treatment adherence thus sits squarely within the cognitive psychology of goal pursuit, and the interventions that work are those that help a patient bridge the recurring gap between a goal they genuinely hold and the specific future acts that would fulfil it.

Current Directions

The most active front is the measurement and targeting of adherence at the level of its phases. The ABC taxonomy made it possible to ask which phase an intervention affects, and newer work uses electronic monitoring, pharmacy data, and digital tools to detect in near-real time whether a patient has failed to initiate, is implementing poorly, or has stopped, so that support can be directed to the specific failure rather than applied generically (Vrijens et al., 2012) (Kvarnström et al., 2021). The promise is precision; the recurring hazard, well recognized in the adherence literature, is that richer monitoring can slide into surveillance and erode the very concordance it was meant to support if it is used to police patients rather than to understand and help them.

A second direction refines interventions toward the multi-component, tailored, and sustained forms the evidence favours, and away from the one-off reminders that the trials found wanting (Nieuwlaat et al., 2014) (Conn & Ruppar, 2017). The convergence of the belief-based and the phase-based strands points toward a model of adherence support that identifies a patient's necessity-concerns balance and the phase at which their regimen is failing, then matches the response to both — and reserves intensive, sustained contact for the patients whose non-adherence signals a genuine clinical or social need rather than a lapse of memory (Kini & Ho, 2018).

Common Misconceptions

Non-adherent patients do not care about their health.

Much non-adherence is a failure to convert a genuine intention into a recurring action, or the product of a reasoned worry about the treatment, rather than indifference: adherence is governed by the balance a patient strikes between a treatment's perceived necessity and their concerns about it, so a patient can care deeply and still not follow the plan (Horne et al., 2013).

Better-informed patients automatically adhere.

Adherence is only weakly a knowledge problem; patients act on their beliefs about the treatment and the felt burden of their condition, so simply supplying more information rarely changes behavior unless it shifts those beliefs (Foot et al., 2016) (Kvarnström et al., 2021).

Adherence is a single yes-or-no behavior.

It is a cascade of distinct phases — acceptance, initiation, implementation, persistence — and a regimen can fail at any one; because the phases multiply, the overall rate is far lower than any single phase, and the remedy differs by phase (Vrijens et al., 2012).

There is an intervention that reliably fixes non-adherence.

The authoritative evidence found that even the most effective interventions are complex and labour-intensive and that their effects are inconsistent and generally small; improving adherence is sustained, tailored work, not a single reliable fix (Nieuwlaat et al., 2014) (Conn & Ruppar, 2017).

Glossary

Acceptance.

The gateway phase in which a patient takes up a recommended plan in the first place; the behavior named by the MeSH descriptor Patient Acceptance of Health Care.

Adherence.

The extent to which a patient's behavior matches agreed recommendations; the preferred modern term, keeping the behavior while dropping the implication of obedience.

Compliance.

The degree to which a patient follows a provider's advice or prescription; the oldest term in the field, criticized for implying a one-way, obedience-based relationship.

Concordance.

The agreement reached between prescriber and patient about whether and how to treat; a prior negotiation rather than the treatment behavior itself.

Health belief model.

A framework in which health action follows from weighing perceived threat against the perceived benefits of and barriers to acting.

Implementation intentions.

Explicit if-then plans that bind a specific future action to an anticipated situational cue, delegating a goal's initiation to that cue; a cognitive remedy for the execution side of the intention-behavior gap.

Implementation.

The phase of adherence describing how closely a patient's actual dosing, once started, matches the prescribed regimen.

Initiation.

The phase of adherence describing whether a patient takes the first dose of a newly prescribed treatment at all.

Intention-behavior gap.

The discrepancy between what a person intends and what they do; non-adherence is a clinical instance of it, re-enacted at every scheduled act.

Necessity-concerns framework.

The account holding that adherence is governed by the balance between how necessary a patient judges a treatment and how concerned they are about its harms.

Non-adherence.

Departure from an agreed plan, whether by never starting, by executing it incorrectly, or by stopping early; averaged across conditions it affects roughly a quarter of patients.

Persistence.

The phase of adherence describing how long a patient continues a treatment before discontinuing it.

Proportion of days covered (PDC).

A pharmacy-based adherence measure giving the fraction of days in a period for which a patient had the treatment available, commonly judged against an 80% threshold.

Prospective memory.

Memory for actions to be performed in the future at an appropriate moment; the cognitive capacity on which scheduled, cue-dependent dosing depends.

Three-phase (ABC) taxonomy.

The consensus division of adherence into initiation, implementation, and persistence, which allows a regimen's failure to be located in a specific phase.

Key Researchers

Icek Ajzen

(University of Massachusetts Amherst). Author of the theory of planned behavior, which models the behavioral intention whose weak link to action is the intention-behavior gap that non-adherence instantiates at every scheduled act. ORCID · Scholar · Faculty · Wikipedia

R. Brian Haynes

(McMaster University). A founder of evidence-based medicine and senior author of the Cochrane review on interventions to enhance medication adherence, whose central finding is how modest and labour-intensive even the effective interventions are. ORCID · Faculty · Wikipedia

Rob Horne

(University College London). Formalized the necessity-concerns framework and the Beliefs about Medicines Questionnaire, establishing the balance between a patient's perceived necessity and concerns as a proximal driver of adherence. Faculty · Wikipedia

Howard Leventhal

(Rutgers University). Developed the common-sense model of self-regulation, which explains why patients act on their own representation of an illness rather than the clinician's, a central reason sensible-seeming regimens are abandoned. Faculty · Institute

Bernard Vrijens

(AARDEX Group and University of Liège). A biostatistician who led the consensus that split adherence into initiation, implementation, and persistence, the ABC taxonomy now standard in the field. Repository

Frequently Asked Questions

What is treatment adherence and compliance?

It is the broad class of behaviors through which a patient carries out, or departs from, a health provider's recommendations for prevention, diagnosis, or treatment, including taking medicines, keeping appointments, following lifestyle advice, and the acceptance or refusal that surround them (DiMatteo, 2004).

How is it different from medication adherence or patient compliance?

Those are narrower behaviors nested beneath this heading: patient compliance and medication adherence name specific medicine-taking behaviors, while treatment adherence and compliance is the umbrella class that also covers appointment-keeping, acceptance, satisfaction, interruption, and refusal (Vrijens et al., 2012).

How common is non-adherence?

Averaged across conditions, about a quarter of patients fail to follow recommendations, and adherence to long-term therapy for chronic disease averages only around half, so non-adherence is a routine feature of care rather than a minority problem (DiMatteo, 2004) (Osterberg & Blaschke, 2005).

Why do patients not follow treatments they agreed to?

Whether a patient follows a long-term treatment is governed largely by the balance between how necessary they judge it and how concerned they are about its harms, together with forgetting, cost, side effects, and regimen complexity, a mix of belief and circumstance rather than a lack of will (Horne et al., 2013) (Foot et al., 2016).

What are the phases of adherence?

A consensus taxonomy divides adherence into initiation (taking the first dose), implementation (executing correctly over time), and persistence (continuing rather than stopping early), preceded by acceptance of the plan; a regimen can fail at any one, and because the phases multiply the overall rate is far lower than any single phase (Vrijens et al., 2012).

What role does memory play?

A large one. Forgetting is among the most commonly cited reasons for missed doses, and every scheduled dose is a prospective-memory task, a future action that must be remembered at the right moment, so the cognitive demands of a complex regimen compound across each repetition (Zogg et al., 2012).

Does non-adherence actually affect outcomes?

Yes. Non-adherence worsens clinical outcomes, drives avoidable hospitalization, and wastes a substantial fraction of health expenditure, which is why it is treated as a determinant of outcome and not merely a marker of sicker patients (Nieuwlaat et al., 2014).

Can anything reliably improve adherence?

Interventions help, but the authoritative evidence is sobering: the most effective are complex and labour-intensive, their average effect is modest, and no single approach works across the board, so improving adherence is sustained, tailored work (Nieuwlaat et al., 2014) (Conn & Ruppar, 2017).

References