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1. Introduction

Preventive and routine health behaviors – such as periodic blood tests, dental check‐ups, cancer screening, triggered pain, cardiovascular monitoring, and lifestyle evaluation – are widely recommended by health authorities to reduce morbidity, identify disease early, and promote long‐term well‐being. Yet many individuals fail to engage in such behaviors at recommended intervals. The gap between knowing what is good for you and doing it has been a persistent challenge in health psychology and public health. Beyond information/knowledge deficits, behavioral‐psychology research emphasizes a richer set of motives for health neglect. These motives may include low perceived risk, discounting of future benefits, fear or anxiety about discomfort or diagnosis, logistical and financial barriers, and habitual non‐engagement. Given the public‐health importance of preventive behaviors, it is timely to review the evidence on why people do not take care of their health, from a behavioral‐psychology lens.

This brief research review has three aims: (1) to summarize major theoretical frameworks used to explain health‐neglect behaviors (cognitive, motivational and structural), (2) to present empirical evidence on motives across domains, and (3) to discuss intervention implications and identify research gaps. By integrating decision‐science (temporal discounting, biases), self‑regulation, affective processes, and structural influences, we aim to provide a holistic behavioral‑psychology account of health‐neglect motives.

2. Theoretical Frameworks for Understanding Health‐Neglect

Several frameworks from behavioral psychology and health psychology were designed to account for and organize the motives for failing to engage in healthy behaviors.

Health Belief Model (HBM). The HBM is one of the oldest and most extensively used frameworks for preventive health behavior. It posits that action is influenced by perceived susceptibility (risk of condition), perceived severity (seriousness of condition), perceived benefits of action, perceived barriers to action, cues to action (events that trigger the behavior), and self‐efficacy (confidence in ability to act) (Becker, 1974 ^[1]; Janz & Becker, 1984 ^[2]). Empirical review shows the HBM retains predictive power: for example Janz & Becker (1984 ^[2]) found substantial support for HBM in preventive behavior studies. More recent meta‐analyses show that the HBM explains, in screening contexts, average R² around .55 for intention (range .25–.89) (Ritchie et al., 2021 ^[3]). For example, when people do not act (e.g., skip a test), often low perceived susceptibility or high perceived barriers are implicated. The HBM emphasizes reflective motive structures (beliefs, expectations) and remains a foundational framework for the discussed topic.

Theory of Planned Behavior (TPB). The TPB (Ajzen, 1991 ^[4]) emphasizes intention as the proximal determinant of behavior, with intention shaped by attitude toward the behavior, subjective norms, and perceived behavioral control. In the health‐care context, TPB helps explain gaps between favorable attitudes and non‐action: when perceived control is low, or when norms/intentions do not translate into action, behavior fails to occur. Reviews show TPB explains some variance in screening behavior, but often less than HBM in screening contexts (mean R² about .24 for behavior and about .46 for intention) (Ritchie et al., 2021 ^[3]). The TPB adds the dimension of social influence and control beliefs, which are important when motives for neglect include low self‐efficacy or strong environmental barriers.

COM‑B / Behavior Change Wheel. More recently, the COM‑B model (Capability, Opportunity, and Motivation leads to Behavior) provides a more integrated framework linking psychological capability (knowledge, skills), physical/social opportunity (resources, access, environmental constraints), and reflective/automatic motivation (beliefs, habits, emotions) (Michie, van Stralen & West, 2011 ^[5]). Under COM‑B, failure to engage in health behavior may be due to lack of capability (e.g., low health literacy), opportunity (e.g., cost or access), or motivation (e.g., low perceived benefit, fear). COM‑B is especially valuable because it explicitly links motive structure with environmental and systemic factors, emphasizing that motives are embedded in context.

Decision‐science / temporal discounting and biases. Traditional frameworks assume reflective deliberation. However, decision‐science emphasizes that individuals often exhibit other cognitive biases – e.g., present bias (preference for immediate rewards over future gain), optimism bias (underestimating personal risk), and status‐quo bias (inertia) – which lead to under‐investment in preventive behaviors whose benefits are delayed and uncertain. For example, people may delay or avoid blood tests because the immediate cost (time, discomfort) looms larger than the distant benefit of early detection. Empirical work on time preferences shows that present bias strongly predicts risky behaviors and low preventive engagement (e.g., Laibson, 1997 ^[6]; O’Donoghue & Rabinson, 1999 ^[7]). More recently, experimental work shows present bias is observable for healthy/unhealthy rewards (present vs. future food, health) (Cheung et al., 2022 ^[8]). Thus, motives for neglect must include bias‐driven devaluation of future benefits.

Self‐regulation and habit. Behavioral psychology emphasizes that translation of intention into action often fails due to self‐regulatory problems: procrastination, avoidance of aversive tasks, low self‐control, and weak implementation intentions. For example, one meta‐analysis found that nearly 47.6% of participants with intentions in a physical‐activity context failed to turn intention into action (Feil et al., 2023 ^[9]). In health care, scheduling blood tests, making and keeping appointments, flossing, keeping hygiene in general, etc., require planning, cueing, habit formation, and self‐monitoring. Motives for neglect thus include lack of implementation intentions, aversion for the task, weak habits, and forgetting.

Affective and automatic motives. Emotions and visceral responses matter: fear of diagnosis, anxiety about procedures (such as needles or dentists), disgust, embarrassment, and fatalism may lead to avoidance. Some models such as the Health Action Process Approach (HAPA) emphasize that affective risk perceptions and coping planning matter (Schwarzer, 2008 ^[10]). For dental care in particular, dental anxiety is a well‐documented barrier. In addition, automatic motivations (habits, cues, environmental triggers) influence whether behavior occurs; if maintaining dental hygiene is not habitual, or environmental cues are weak, neglect becomes more likely.

Social‑structural motives. Finally, motives for neglect are embedded in social, economic, and structural contexts: cost and insurance coverage, access and convenience, health literacy, cultural beliefs, mistrust, stigma, and provider communication. These factors may moderate or mediate the effect of individual motives. For example, even if someone believes in the benefit of a blood test, lack of accessible appointments or high out‐of‐pocket cost may deter action – reflecting opportunity constraints (in COM‑B terms) or perceived barriers (in HBM terminology).

Together, these frameworks indicate that motives for not taking care of one’s health often involve a mixture of low motivation, impaired self‐regulation, cognitive biases, affective burdens, and structural constraints. The remainder of the review uses these vantage points to examine empirical evidence on motives for non‐engagement in health care.

3. Decision‐Science Motives: Temporal Discounting and Biases

From a behavioral‐economics perspective, one of the strongest drivers of preventive‐care avoidance is present bias or hyperbolic discounting: the tendency to favor immediate comfort or savings over delayed benefits. Preventive behaviors typically have immediate costs (time, discomfort, money) while benefits accrue in the uncertain future (disease avoided, early detection). Decision‐science models show that people systematically undervalue future benefits relative to immediate costs, leading to procrastination or avoidance (Laibson, 1997 ^[6]). Empirical work shows that present bias applies not only to monetary rewards but also to health/dietary decisions: for example, a large‐scale experiment found strong present bias in reward contexts involving food and predicted real‐world behaviors such as alcohol consumption (Cheung et al., 2022 ^[8]).

In the preventive health context, this translates into motives like: “I’ll get the blood test later; I feel fine now”, or “It’s just a check‐up—why spend time on it today?” The immediate minor inconvenience looms larger than the vague future benefit.

Optimism bias (the belief that one is less at risk than others) further reduces motivation for preventive action. Weinstein and others documented that people often perceive their own risk as lower than that of peers, reducing the perceived urgency of action (Weinstein, 1987 ^[11]). Real‐world screening studies show that optimistic risk perception is a consistent barrier. Thus, biases lead to downward‐biased anticipated benefit and upward‐biased perceived barriers, making non‐action more likely.

4. Self‑Regulation Failures and Habitual Avoidance

Even when individuals intend to engage in preventive behavior, self‐regulation failures and habit formation issues frequently undermine that intention. Implementation‐intention theory shows that specifying a cue (“If it’s the first Monday of the month, I’ll book the blood test”) helps convert intention into action (Gollwitzer, 1999 ^[12]); lacking such planning increases risk of procrastination.

Procrastination research shows health‐related tasks (appointments, tests) which are perceived as aversive or inconvenient are often delayed despite acknowledgement of future cost (Steel, 2007 ^[13]). For example, making a dental appointment may involve dental anxiety, phone calls, transportation – making it aversive relative to doing nothing now. Over time, delay becomes habitual: missed visits accumulate, perceived need declines, and avoidance becomes reinforced.

Meta‐analytic evidence confirms large intention–behavior gaps: in physical‐activity contexts, about 47.6% of intenders fail to act (Feil et al., 2023 ^[9]). While not identical to screening or dental attendance, the pattern is suitable to generalization: intentions alone are insufficient, especially for preventive behavior.

Habit theory adds that preventive behaviors require stable cues and repetition; where cues are weak (e.g., “I’ll remember to book the test when I feel like it”) the behavior is vulnerable to omission. Forgetting, competing demands, and weak routines all contribute. Interventions that provide external cues (reminders) or restructure the environment (easy booking, default appointments) help to overcome this.

5. Affective and Sensory Motives: Fear, Disgust, Avoidance

Motives for non‑health‑care behaviors often include affective barriers. Fear of diagnosis (e.g., “If the test finds something, I can't cope”), fear of procedure (needles, pain), dental anxiety (for check‐ups/cleanings) and embarrassment/stigma are significant. For example, dental anxiety is a prominent barrier to regular dental care (Armfield, 2013 ^[14]). Avoidance may serve a short‐term emotion‐regulation function (“I’ll not think about it”), albeit at long‐term cost.

Disgust and bodily concerns may reduce willingness to submit bodily samples (blood, stool) or to attend oral‐health care where gums, saliva are involved. Some research into self‑sampling for infections finds that even when acceptability is high, discomfort about body fluids or invasive methods reduces uptake (Borek et al., 2025 ^[15]). Additionally, fatalism (“What's the point? If something’s wrong it’ll show up itself”) and embarrassment (“I don’t want someone looking at my teeth/gums”) may dampen motivation.

From a behavioral‐psychology perspective, affective avoidance intersects with self‐regulation: tasks that evoke negative affect are more likely to be delayed or avoided and less likely to benefit from simple planning.

6. Social, Economic, and Structural Motives

While many motives operate at the individual cognitive/affective level, it is vital to recognize that motives for neglect are shaped by structural and social contexts:

a) Cost and insurance coverage. Financial barriers remain a major reason for failing to engage in preventive care; affordability is a documented barrier across many studies of medical (or dental) access.

b) Access, convenience and system navigation. Difficulty booking an appointment, long waits, transportation issues, clinic hours incompatible with work, complexity of referral—all reduce opportunity (in COM‑B terms) and act as perceived barriers (HBM). A systematic review of cardiometabolic health checks found that practical issues (time constraints, appointment issues) were prominent barriers (de Waard et al., 2018 ^[16]).

c) Health literacy and numeracy. Low health literacy reduces comprehension of risk and benefit, reducing perceived susceptibility and benefit, increasing perceived barriers. Numeracy deficits make risk communication less effective.

d) Cultural beliefs, norms, and mistrust. In some communities, cultural or religious beliefs reduce engagement (“My health is God’s will”), or mistrust of health systems reduces motivation. Studies of screening uptake among ethnic minorities highlight the role of culture, language, and trust (Tatari et al., 2020 ^[17]; Hu et al., 2023 ^[18]).

e) Social norms and role modeling. If preventive care is not normative in one’s peer group or community, subjective norms (in TPB) or social motivation (in COM‑B) may be weak, reducing intention.

f) Work/life demands and opportunity cost. For many people, the immediate cost (taking time off work, transport, childcare) outweighs the distant benefit. Lower‐income individuals may discount the future more steeply, making present bias and structural constraints compound each other.

Thus, structural motives may reduce opportunity to act or inflate perceived barriers, even if motivation is present.

7. Habit, Automatic Processes, Forgetting and Maintenance

Beyond reflective motives lies the domain of automatic behavior. Health maintenance behaviors (e.g., regular medical visits, annual tests and checks) require establishing routines and cues. Without stable cues or environmental triggers, forgetting is common. The conversion of intention into habit requires repetition and reinforcement; lacking this, preventive actions revert to “when I remember” mode and are often omitted.

Additionally, outcome feedback is delayed (you may have a normal result, so no positive reinforcement), which weakens habit formation. The absence of immediate reward undermines habit strength, making the behavior fragile. From the behavioral‐psychology viewpoint, then, neglect arises from weak cue–response links, low reinforcement, competing routines, and a lack of automaticity.

8. Specific Sub‐Topics, Their Motives and Mechanisms

Two of the most addressed preventive-health neglect sub-topics (provided their importance and prevalence) are regular blood testing and dental care.

8.1. Regular Blood Testing (Routine Monitoring and Screening)

Empirical evidence shows that adherence to recommended regular blood tests (for e.g., dyslipidaemia, type 2 diabetes, hepatitis C, HIV, etc.) remains sub‐optimal. A recent systematic review (Le et al., 2025 ^[19]) found the following median adherence rates: 66.3 % for diabetes screening, 67.8 % for dyslipidaemia, 34 % for hepatitis C, and 36.8 % for HIV; for PSA screening in men about 37.2 % (Le et al., 2025 ^[19]). These findings indicate that even relatively “easy” blood tests are missed frequently.

In literature, the following motives are at play for this neglect:

Present bias and delay of benefit. People may feel fine, do not “see” the benefit of the test today, and prefer to postpone.

Low perceived susceptibility/severity. Many feel, “I don’t have symptoms, so why test?” According to HBM, lower perceived susceptibility reduces action. Indeed, de Waard et al. (2018 ^[16]) found that low perceived risk or feeling healthy were barriers to cardiometabolic health checks.

Perceived barriers. Time, effort, discomfort of blood draw, transport, cost. These perceived barriers often outweigh the perceived benefit.

Low self‐efficacy or behavioral control. If patients perceive they cannot arrange the visit or coordinate the test, they may avoid booking it.

Structural constraints/opportunity issues. Access to phlebotomy services, clinic hours, transportation, and insurance coverage shape opportunity (COM‑B). For example, if blood test centers are not conveniently located, the opportunity cost may deter people.

Forgetfulness/habit failure. Routine yearly tests may require scheduling; without a cue or habit, they are easily delayed.

Optimism bias. Many individuals believe they are at lower risk than peers, reducing urgency.

Affective avoidance. Some may fear needles or results and delay testing to avoid dealing with potential bad news.

As for intervention evidence, studies show that reminders, extended clinic hours, point‐of‐care testing (reducing delay to benefit) and small incentives improve uptake, although the variation is large and implementation contexts matter.

8.2. Dental Care (Preventive Visits, Hygiene Adherence)

Empirical evidence shows that dental attendance for preventive check‐ups is uneven. Systematic reviews show multiple barriers. For example, people with disabilities report cost, inaccessibility, fear/anxiety, transport, communication issues as major barriers (Agarwal et al., 2024 ^[20]). For culturally and linguistically diverse (CALD) careers, affordability was the foremost barrier, followed by negative provider experiences and language/communication issues (Marcus et al., 2022 ^[21]). Dental‐care neglect thus is widespread and structurally embedded.

The following motives are specific to dental care neglect:

Aversion/fear. Dental anxiety is a major motive for avoidance; this affective barrier is strong and can establish a pattern of avoidance.

Perceived low benefit and low illness salience. Many skip dental check‐ups because “teeth are fine” or “no pain” – low perceived need.

Cost and economic barriers. Dental services are often not fully covered; the immediate cost looms large.

Access and opportunity cost. Transport, time off work, waiting lists – all raise the barrier.

Low habitual routine. Unless dental check‐ups are embedded in a regular routine, they are neglected.

Optimistic bias. People may underestimate the risk of dental disease and not see preventive visits as urgent.

Structural inequalities. Disadvantaged groups have higher dental‐neglect due to compounded cost, access, literacy and trust issues (Peres et al., 2019 ^[22]; Watt, 2012 ^[23]; Palència et al., 2014 ^[24]; Listl, 2011 ^[25]).

As intervention evidence, effective strategies for dental care often combine anxiety‐reduction (CBT for dental fear), subsidized services or insurance coverage, appointment reminders, and integration with primary‐care settings to reduce friction.

Other preventive-health neglect sub-topics addressed intensively in the literature are cancer screening, cardiovascular and metabolic monitoring, pain-triggered care, genetic risk testing, and vaccination.

8.3. Cancer Screening

a) For breast cancer screening, U.S.P.S.T.F medical authority recommended mammography every 1–2 years (age 40/45–74), but only about 65% or the female population is up to date (CDC, 2024) and 55–82% in Europe, depending on jurisdiction (OECD, 2023 ^[26]).

The motives specific for this neglect were found to be:

Affective avoidance. The fear of diagnosis leads to self-protective non-attendance (Vrinten et al., 2017 ^[27]).

Temporal discounting. Delaying by “I’ll do it next cycle” leads to intention decay in time (Loewenstein & Prelec, 1992 ^[28]).

Anticipated pain. The mammography pain expectations suppress booking it (Whelehan et al., 2013 ^[29]).

Magical thinking or optimistic bias. The irrational belief “If I don’t test, I don’t have cancer” suppresses intention (Weinstein, 1980 ^[30]).

b) For colorectal cancer screening (FOBT, FIT, sigmoidoscopy, colonoscopy), global compliance is particularly poor. In the U.S., 59% of the population is up to date (Joseph et al., 2020 ^[31]), while in Europe between 23–67% depending on postage FIT roll-out (Arnold et al., 2017 ^[32]).

The main motives as behavioral inhibitors are:

Disgust (stool). Disgust sensitivity predicts dropout (Olatunji, 2015 ^[33]).

Procedural imagination aversion. The colonoscopy imagery leads to avoidance. This motive is also related to social embarrassment (Consedine et al., 2011 ^[34]) and anticipated sedation/anesthesia risk exaggeration (risk amplification bias).

c) For cervical cancer screening (and HPV self-sampling), it was found that HPV self-sampling reduces friction, and hence repeatedly increases uptake in RCTs (Arbyn et al., 2018 ^[35]; Wang et al., 2019 ^[36]). When friction is removed, the behavior moves.

8.4. Cardiovascular and Metabolic Monitoring

Cardiovascular and metabolic monitoring includes the most usual tests and measurements such as hypertension monitoring on intervals, HbA1c testing (for diabetes), and lipid panels. It is surprising (and worrying) that avoidance rates are worst among those with the most risk, which is explained by the inverse prevention law (Victora et al., 2000 ^[37]).

One of the highest friction patterns is that if the test is “normal most of the time”, then people infer “doesn’t matter.” This normal result paradox stands as a negative reinforcement for not testing.

As for the mechanisms underlying this behavior, Gigerenzer & Gaissmaier (2011 ^[38]) proposed that regression to the mean is not understood; Dawson et al., 2006 ^[39] advanced the ambiguity aversion (Ellsberg-type), defined as people not wanting uncertain health info; and Dai et al. (2014 ^[40]) advanced the concept of passive non-action, claiming that people do not “decide not to get tested”, but they “never allocate time”.

8.5. Pain-Triggered Care

Even though it is fair to hypothesize that pain should increase care seeking, it was found that about 30–60% of people with persistent pain do not consult within first six months (Dionne et al., 2008 ^[41]; Jordan et al., 2010 ^[42]).

Among the main motives detected are:

Anticipated stigma. It is a social threat processing (“I’ll be seen as exaggerating”) (Salovey et al., 2002 ^[43]).

Identity protection. Self-signaling (“If I don’t go, I’m a healthy person”) — self-signaling (Bénabou & Tirole, 2006 ^[44])

The crucial finding is that pain does not reliably force behavior, because fear of medical findings can exceed fear of symptoms.

8.6. Genetic Risk Testing

For example, BRCA1/2 referral after pedigree screening has a completion rate of about 25–35% (Manickam et al., 2018 ^[45]).

The main motives are: maintaining positive affect (Sweeny et al., 2010 ^[46]; Golman et al., 2017); anticipatory regret (Zeelenberg, 1999 ^[47]); and future-self dissociation (Hershfield, 2011 ^[48]).

Genetic information is the perfect demonstration of hyperbolic discounting plus affective threat.

8.7. Vaccination / Boosters / Adult Immunization

Adult immunization is a high-return preventive behavior, but with systematically high dropout for boosters (Larson et al., 2014 ^[49]; Brewer et al., 2017 ^[50]).

The main motives driving avoidance are: omission bias (Ritov & Baron, 1990 ^[51]); default effects (Madrian & Shea, 2001 ^[52]); and social trust heuristics (Lewandowsky et al., 2017 ^[53]).

9. Summing Up Motives and Mechanisms

Grouping motives in categories identified by related underlying cognitive-psychological mechanisms applying to all sub-topics of preventive-health neglect, we propose the following taxonomy:

Cognitive appraisal failure: Low perceived susceptibility, low perceived benefit, high perceived barriers, low self‐efficacy, all consistent with HBM/TPB studies (Janz & Becker, 1984 ^[2]; Ritchie et al., 2021 ^[3]).

Temporal and decision biases: Present bias, optimism bias, and status‐quo bias lead to postponement and avoidance of preventive actions.

Self‐regulatory failure and habit weakness: Intention-behavior gaps are large (Feil et al., 2023 ^[9]) and, without planning, cues and reinforcement, preventive behaviors fail to translate from intention to action.

Affective and visceral avoidance motives: Fear, anxiety, disgust, embarrassment lead to avoidance or delay.

Structural/social motives: Cost, access, health literacy, cultural beliefs, trust and convenience moderate or override individual motives; they are inherent in COM‑B’s opportunity component.

Automatic/habitual process deficits: Preventive behaviors are not yet routine; the absence of stable cues means forgetting or deprioritising gains.

There is interactivity and heterogeneity of motives. Motives rarely act in isolation. For example, low health literacy may increase perceived barriers, higher cost amplifies present bias, cultural norms may reduce perceived susceptibility.

Crucially, single‐factor explanations (e.g., “people don’t go because they don’t know”) are insufficient. It follows that multi‐component interventions that address multiple motives simultaneously are more likely to succeed.

Intervention implications and evidence

Understanding motives helps guide intervention design. Behavioral‐psychology suggests that effective interventions should reduce barriers, strengthen motivation, support self‐regulation, and improve opportunity. The intervention strategies that have empirical support are listed briefly in what follows:

Reducing friction/opportunity cost: Simplifying booking systems, offering extended hours, walk‐in clinics, mobile services, and point‐of‐care testing. These are argued to reduce opportunity barriers (de Waard et al., 2018 ^[16]; Le et al., 2025 ^[19]).

Reminders and defaults: Automated reminders (SMS, email) and default scheduling (automatic appointments) exploit cueing and inertia, helping to overcome forgetting or procrastination.

Small immediate incentives or salient benefit feedback: Providing immediate reward or feedback for completing a preventive action combats present bias by increasing the salience of short‐term benefits.

Addressing affective barriers: Interventions for dental anxiety (CBT, exposure, distraction) reduce fear and enhance engagement; for blood testing, some use desensitization or patient‐friendly phlebotomy.

Improving self‐efficacy and planning: Implementation‐intention interventions (“If this, then I will do that”), action‐coping planning, and motivational interviewing increase the likelihood of action.

Communication and risk‐framing: Using absolute risk, visuals, and personalized feedback improves risk comprehension, counteracts optimistic bias, and strengthens perceived susceptibility and benefit (Ritchie et al., 2021 ^[3]).

Cost/insurance policy: At the structural level, reducing out‐of‐pocket cost for preventive services improves uptake (especially dental care) by removing one of the largest barriers.

Cultural and literacy tailoring: Tailoring interventions to the language, culture, and literacy level of target populations addresses social motives (Marcus et al., 2022 ^[21]).

Multi‐component integrated interventions

Meta‐analysis of health‐promotion interventions targeting multiple behaviors found that interventions covering multiple domains show better effect (e.g., unhealthy behaviors) though diminishing returns at high numbers of simultaneous targets may occur (Wilson, 2015 ^[54]).

In preventive care contexts, combining several strategies (for instance, reminders plus simplification plus incentives plus planning) tends to be more effective than single strategy alone (Yakoubovitch et al., 2023 ^[55]).

From the empirical meta‐analyses, a systematic review of behavioral interventions for screening colonoscopy found that patient navigation and multicomponent interventions increased completion by about 54% compared to controls (Yakoubovitch et al., 2023 ^[55]). This suggests that addressing multiple barriers (navigation, reminders, planning, and so on) is beneficial.

For example, in the dental domain, combining anxiety‐reduction with reduced cost, reminder, and default scheduling is typically more effective than purely informational campaigns.

Importantly, intervention design should be guided by assessment of dominant motives in the target group: e.g., high anxiety warrants focused anxiety‐reduction; cost‐sensitive populations need subsidy; groups with low perceived risk need risk‐communication; and groups with access issues need system redesign.

10. Limitations in the Literature and Research Gaps

Staying within the behavioral psychology framework, despite significant progress, several limitations in the literature remain, reflecting also research gaps:

Heterogeneity in measures and behaviors: Studies vary widely in how they define and measure preventive behaviors, barriers, and motives, which complicates synthesis across domains.

Intention–behavior gap remains large: Although intention is necessary, many interventions still struggle to convert intention into sustained behavior (Feil et al., 2023 ^[9]; Ritchie et al., 2021 ^[3]). The majority of studies examine uptake of a single preventive action, not maintenance across time.

Under‐representation of structural/inequality issues in behavioral frameworks: Many behavioral studies remain individual‐level, neglecting system‐level or policy‐level motives for neglect (e.g., insurance design or provider supply). The COM‐B model emphasizes opportunity, but empirical work often stops at individual barriers.

Limited long‐term follow‐up and habit formation data: Few studies evaluate how preventive behaviors are maintained over years or how habit formation can be supported.

Equity and context specificity: Many studies come from higher‐income countries and less is known about low‐ and middle‐income settings, or about diverse cultural contexts. For example, screening motives in culturally diverse groups often include language and cultural barriers but are under‐researched (Marcus et al., 2022 ^[21]).

Mediation and mechanism‐testing are scarce: While many interventions report outcomes, fewer examine which psychological mechanisms (e.g., increased self‐efficacy, reduced fear, etc.) mediate behavior change. Stronger mechanism‐based work would help refine theory and design.

Focus on single behaviors rather than integrated preventive care: Real‐world health maintenance often involves multiple behaviors (blood tests, dental visits, cancer screening, lifestyle, and so on). There is limited research on integrated preventive‐care behavior and the overlapping motives across domains (Ritchie et al., 2021 ^[3]).

Cost‐effectiveness and scalability: While many interventions show efficacy, fewer studies examine cost‐effectiveness, sustainability, and adaptation in routine clinical practice or public systems.

11. Future Research Directions and Methodology

In the light of the above limitations and gaps, several research directions can be highlighted for being designed in the future, which we grouped in methodological types.

Mechanism‐based trials: Design and test interventions that explicitly target identified motives (e.g., present‐bias reduction, implementation intention planning, or fear‐reduction) and assess whether changes in those motives mediate behavior change.

Adaptive/personalized intervention design: Develop models to identify dominant motives for health‐neglect in individuals (e.g., cost versus fear versus forgetting) and tailor interventions accordingly, rather than one‐size‐fits‐all.

Longitudinal habit‐formation research: Investigate how preventive behaviors transition from intention to habit, the role of reinforcement, stable cues, and context change (e.g., life transitions) in maintaining behavior.

Equity‐focused research: Engage under‑represented populations (cultural/ethnic minorities, low‑income, rural) to understand how structural motives (cost, trust, access) combine with psychological motives for non‐engagement, and test interventions that address both.

Integrated preventive‐care behavior research: Instead of studying single behaviors, examine packages of preventive actions (blood tests, dental visits, and other screenings) to identify common motives and opportunities for intervention and to design integrated care pathways.

System‐level and policy evaluation: Examine how health‐system design (insurance coverage, appointment systems, provider incentives) influences individual motives and behavior, and evaluate cost‐effective, scalable interventions at the policy level.

Implementation science and real‐world scale‑up: Move beyond efficacy to evaluate how behavioral‐psychology interventions can be embedded in health systems, their cost‐effectiveness, sustainability, and adaptation to different populations and settings.

At a meta-theoretical and methodological analysis, we found that the past research on the topic of motives for health care neglect and their underlying mechanisms, entwined empirical and theoretical, was conducted and remained within a mere psychological framework, although psychiatry and a limited group of cognitive sciences contributed marginally.

Given that the topic is of utmost importance for health-care policies, a stronger interdisciplinary collaboration is required to go deeper into the mechanisms responsible for avoidance behaviors. In this respect, especially neurosciences and associated chemistry fields are called to contribute to the research directions mentioned above in order to fill the signaled gaps beyond the empirical setups. The main argument supporting this call is that the matter of the discussed research falls within the more general topic of people behaving in unhealthy ways in condition of awareness about possible harms, including addictions, which benefited in effective mode by the contributions from the aforementioned disciplines.

12. Conclusion

Motives for failing to take care of one’s health are not simply a matter of ignorance or laziness. Behavioral‑psychology reveals a rich tapestry of interlocking motives: cognitive appraisals, temporal biases, self‐regulatory failures, affective avoidance, structural barriers, and habit/non‑habit dynamics. Effective preventive behavior requires more than intention; it requires planning, cues, support, access, and reinforcement. The best interventions recognize the multi‐level nature of the problem and address the psychological, behavioral, and structural motives simultaneously. For public health and clinical practice, this means moving beyond simple information campaigns toward comprehensive, tailored, and context‐sensitive approaches. Future research should emphasize mechanism‐driven, equitable, and scalable designs to reduce the gap between knowing and doing. Only by understanding and addressing the full range of behavioral motives can we hope to significantly increase preventive health behavior uptake and improve population health. At foundational level, meta-theoretical and methodological analyses are required to extend the interdisciplinary collaboration in the research of the topic, which is currently limited.