When Medical Knowledge Is Not Enough: Medication Adherence and Self- Treatment Among Physicians With Chronic Illness in Upper Egypt

When Medical Knowledge Is Not Enough: Medication Adherence and Self- Treatment Among Physicians With Chronic Illness in Upper Egypt | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When Medical Knowledge Is Not Enough: Medication Adherence and Self- Treatment Among Physicians With Chronic Illness in Upper Egypt Mahomoud Ahmed Bekiet, Ahmed Jado Nabih Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8888407/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Medication adherence in chronic illness is influenced by behavioral, psychological, and contextual factors. Whether physicians—despite high medical literacy—demonstrate superior adherence compared with non-physician patients remains unclear. This study compared medication adherence between physicians with chronic illness and matched non-physician controls and examined psychological and behavioral correlates, including self-treatment practices. Methods A cross-sectional comparative study was conducted among 250 physicians and 250 age-, sex-, and disease-category–matched non-physician adults receiving long-term pharmacotherapy at university outpatient clinics in Upper Egypt. Self-reported adherence was assessed using the 8-item Morisky Medication Adherence Scale (MMAS-8) as the primary adherence measure. Self-treatment behavior, depressive symptoms, anxiety, and burnout were evaluated using validated instruments. In a consenting subset, objective adherence was estimated using pharmacy refill data (proportion of days covered [PDC]) and documented pill-count records. Multivariable logistic regression models were used to examine factors associated with adequate adherence. Results The prevalence of adequate self-reported adherence did not differ significantly between physicians and controls (28.4% vs. 31.2%, p = 0.557). Among participants who consented to pharmacy linkage (36% physicians; 43% controls), median PDC and the proportion achieving PDC ≥ 0.80 were comparable between groups. Agreement between self-reported and objective adherence was poor in both cohorts. In adjusted analyses, physician status was not independently associated with adequate adherence. A higher number of prescribed medications and greater engagement in self-treatment behaviors were independently associated with higher odds of adequate self-reported adherence. Conclusions Physicians with chronic illness demonstrated adherence patterns comparable to matched non-physician patients. Professional training and medical knowledge were not independently associated with better adherence. Behavioral engagement and treatment-related factors appeared more strongly associated with adherence than occupational status. The limited concordance between self-reported and objective adherence highlights the importance of multimethod assessment in adherence research. medication adherence physicians chronic illness self-treatment mental health pharmacy refill health behavior Background Medication adherence has become a fundamental pillar of the management of chronic diseases. Still, suboptimal adherence has become the norm in most conditions and health care systems, negatively impacting treatment outcomes and leading to preventable morbidity, health care utilization, and expenditure. According to recent reviews, adherence is not only a behavioral phenomenon but also a dynamic process shaped by patient, therapy, and system-level factors, and it remains a challenge despite the availability of effective therapeutic interventions [ 1 , 2 ]. Recent syntheses indicate that studies on adherence are complicated by varying definitions and measurement methods, which can yield different estimates and make results and situations non-comparable [ 3 , 4 ]. At the same time, physician health is becoming a prominent focus in the post-2020 period, and physicians are exposed to persistent occupational stressors that may affect self-care behaviors. According to the national survey work conducted on a large scale during the COVID-19 period, there were notable changes in physician burnout and work-life integration, which implies that there is a work environment that can potentially result in some deterioration of routine preventive care, follow-up persistence, and maintenance of health behaviors over an extended period [ 5 ]. Physicians are highly health-literate and have favorable access to medical knowledge, which does not always translate into optimal individual health behaviors. Instead, time limitations, symptom normalization, professional stigma, and fragmented care paths may pose specific setbacks to continuous self-management among physicians with chronic disease themselves. The issue of self-treatment and self-medication is particularly applicable when clinicians themselves or they become patients. The new methodological literature underscores that self-medication is not consistently defined across research contexts, making it more difficult to interpret and compare its rates and determinants [ 6 ]. Contemporary systematic research on self-medication during the pandemic indicates substantial differences in prevalence and determinants (e.g., convenience, perceived mild symptoms, barriers to access). Medications prescribed to the population are heterogeneous: analgesics, antibiotics, supplements, and other treatments that can be obtained both in pharmacies and informally [ 7 ]. These trends raise questions about the safety and efficacy of medications, interactions, disease progression, masking, and, more importantly, in chronic conditions, the possible replacement of well-structured longitudinal care with ad hoc care. The other problem is that the approach is critical regarding adherence measurement. The latest literature review of adherence measurement in chronic disease studies indicates that self-report measures remain widely used due to their practicality and affordability. In contrast, objective measures, such as pharmacy refill records, are more accurate but are limited by logistical and connectivity issues [ 3 ]. On the same note, a COSMIN-informed systematic review (cardiovascular disease and type 2 diabetes) highlights inconsistent measurement quality of widely used patient-reported adherence measures and recommends paying close attention to instrument selection and reporting [ 4 ]. These problems are particularly acute in groups such as physicians, where social desirability and professional identity may bias self-reports, and objective validation would be beneficial in such groups. Although physician well-being and clinical outcomes of non-adherence are important, direct evidence comparing medication adherence among physicians with chronic illness using matched non-physician controls is limited [ 1 , 5 ]. This study aimed to explore medication adherence behavior among physicians living with chronic illness compared with matched non-physician controls, and to examine the psychological and behavioral factors associated with adherence, including self-treatment practices. Methodology Study Design and Reporting Framework This study was a cross-sectional comparative observational study conducted in Upper Egypt to evaluate medication adherence and self-treatment behaviors among physicians living with chronic illnesses, compared with non-physician controls. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. The primary objective was to compare medication adherence between physicians and non-physician patients. In contrast, secondary objectives included identifying behavioral and psychological factors associated with adherence and assessing concordance between self-reported adherence and objective adherence measures in a consenting subset. Study Setting Participants were recruited from outpatient clinics affiliated with the Faculty of Medicine, Luxor University, Egypt. Recruitment was conducted through routine outpatient visits to ensure that both physicians and non-physician controls were drawn from similar healthcare environments and had comparable access to outpatient services. Data collection occurred following institutional ethical approval granted in January 2026. The outpatient clinic setting included internal medicine and specialty chronic disease follow-up services, reflecting typical clinical pathways for individuals requiring long-term pharmacotherapy. Participants and Eligibility Criteria Eligible participants were adult individuals (≥ 18 years) diagnosed with at least one chronic illness requiring long-term pharmacological treatment. The physician group consisted of practicing physicians with documented chronic illness receiving ongoing pharmacotherapy. The control group consisted of non-physician adult patients with chronic illnesses recruited from the same outpatient clinical settings. Participants were excluded if they were unable to provide informed consent, had acute medical instability requiring urgent intervention, or had incomplete adherence data that prevented classification of adherence status. The control group was selected using a matching strategy based on age, sex, and disease category. Matching was performed using a 1:1 frequency-matching approach, such that the distribution of these characteristics in the control group approximated that in the physician group. Disease category matching was applied to minimize confounding related to the clinical type of chronic illness and associated medication regimens. Both groups were recruited from the same clinical settings to reduce systematic differences in healthcare access and follow-up intensity. Sample Size Estimation The target sample size was determined based on the minimum number of participants required to detect a clinically meaningful difference in the prevalence of adequate medication adherence between physicians and controls, assuming a two-sided alpha level of 0.05. Based on preliminary assumptions, a minimum of 178 participants per group was estimated to provide adequate statistical power. To account for incomplete responses, missing questionnaire data, and potential exclusions, the planned sample size was increased to 250 participants per group, yielding a total sample of 500 participants. Data Collection Procedures Data were collected using structured questionnaires and clinical record extraction after ethical approval. Participants completed standardized study questionnaires assessing adherence, psychological symptoms, burnout, and self-treatment behavior. Sociodemographic and clinical characteristics, including disease duration, number of chronic conditions, and number of prescribed medications, were collected via participant report and, when available, outpatient medical records. Disease control status was recorded based on documentation in outpatient follow-up records as noted by the treating clinician. Data were anonymized and coded before analysis to ensure confidentiality, particularly in the physician cohort, where privacy concerns could influence reporting. Exposure Definition (Physician Status) The main exposure variable was professional status (physician vs non-physician control). Physicians were defined as individuals with a medical degree who were actively engaged in clinical practice and receiving pharmacotherapy for chronic illness. Controls were defined as adult non-physician patients attending the same outpatient services with comparable chronic illness categories and requiring long-term medication use. This exposure variable was treated as the primary grouping variable in comparative analyses and was also included as a covariate in multivariable regression modeling. Outcomes: Medication Adherence Assessment The primary outcome was medication adherence, assessed using the 8-item Morisky Medication Adherence Scale (MMAS-8), a validated self-report instrument widely used in chronic disease populations. MMAS-8 scores were calculated according to established scoring guidelines, and adherence was categorized using standard cutoffs to define adequate versus inadequate adherence. Secondary adherence outcomes included adherence estimates from additional validated instruments, such as the Adherence to Refills and Medications Scale (ARMS) and the General Medication Adherence Scale (GMAS), which were included to enhance the robustness of adherence assessment across different adherence constructs. In a consenting subset of participants, objective adherence was assessed using pharmacy refill data and pill-count documentation. Pharmacy refill adherence was quantified using the proportion of days covered (PDC), calculated as the number of days during which medication was available divided by the total observation period. A PDC threshold of ≥ 0.80 was used to define adequate objective adherence, consistent with commonly applied pharmacoepidemiologic standards. Pill-count adherence was calculated from documented pill-count records when available. Concordance between subjective adherence (MMAS-8) and objective adherence (PDC) was assessed using Cohen’s kappa statistic and correlation analysis. Assessment of Self-Treatment and Psychological Variables Self-treatment behavior was measured using the Self-Administration Scale for Self-Medication Practices (SAS-SMP), a structured tool designed to quantify the extent of self-directed medication behaviors. Higher scores indicated greater engagement in self-treatment practices, including medication adjustment or self-management without formal clinical supervision. Psychological symptoms were evaluated using validated symptom scales. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), and anxiety symptoms were assessed using the Generalized Anxiety Disorder-7 (GAD-7). Burnout was measured using a standardized burnout scale, and total burnout scores were included in analyses. These psychological variables were incorporated as potential predictors and confounders, given established associations between psychological distress, self-management capacity, and adherence behavior. Covariates and Potential Confounding Factors Prespecified covariates included age, sex, number of chronic conditions, disease duration, number of prescribed medications, and polypharmacy status (defined as ≥ 5 medications). Socioeconomic variables, including education level and monthly income, were recorded as descriptive characteristics given expected differences between physicians and controls. Health insurance status was also included as a covariate due to its potential association with medication access and refill continuity. The inclusion of these covariates was based on clinical plausibility and evidence from adherence literature suggesting that demographic, treatment-related, and psychological factors influence adherence outcomes. Statistical Analysis Continuous variables were summarized as medians and interquartile ranges because of non-normal distributions and compared between groups using the Mann–Whitney U test. Categorical variables were presented as frequencies and percentages and compared using chi-square tests or Fisher’s exact test where expected cell counts were small. A Bonferroni correction was applied to multiple comparisons of baseline characteristics to reduce the likelihood of a type I error. Multivariable logistic regression was performed to identify independent predictors of adequate adherence, with adherence status (adequate vs inadequate) as the dependent variable. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were reported. Variables entered into the regression model were selected based on clinical relevance and literature-informed plausibility rather than automated statistical selection procedures. A separate physician-only multivariable regression model was conducted to examine whether occupational variables, including weekly working hours, number of night shifts per month, and specialty type, were associated with adherence. Complete-case analysis was used for regression modeling, and statistical significance was set at p < 0.05. Agreement between subjective and objective adherence measures was evaluated using Cohen’s kappa statistic, and correlations between MMAS-8 scores and PDC values were assessed using Spearman's rank correlation coefficient. Ethical Considerations Ethical approval was obtained from the Research Ethics Committee of the Faculty of Medicine, Luxor University, Egypt (IRB approval number: Luxmed-070126-108; approval date: 07 January 2026). All participants provided written informed consent prior to inclusion. Participants were informed about the voluntary nature of participation, confidentiality measures, and their right to withdraw at any time without affecting clinical care. For the objective adherence assessment, additional consent was obtained for pharmacy refill data linkage. All study data were anonymized and stored securely, with access limited to the research team. Use of Artificial Intelligence Tools Grammarly was used for language editing and improvement of manuscript clarity. No artificial intelligence tools were used for data generation, statistical analysis, interpretation of findings, or decision-making regarding study conclusions. The authors retain full responsibility for the integrity and content of the manuscript. Results Table 1 Baseline characteristics of physicians and controls Variable Category Physicians (n = 250) Controls (n = 250) p p (Bonferroni) Age (years) 48.0 [35.0–59.0] 49.0 [38.0–57.8] 0.678 1.000 Chronic conditions (n) 2.0 [1.0–3.0] 2.0 [1.0–3.0] 0.262 1.000 Disease duration (years) 10.0 [5.0–14.0] 10.0 [5.0–16.0] 0.523 1.000 Medications (n) 3.0 [2.0–4.0] 3.0 [2.0–4.0] 0.424 1.000 PHQ-9 score 14.0 [7.0–20.0] 14.0 [7.3–21.0] 0.824 1.000 GAD-7 score 10.0 [4.0–16.0] 10.0 [4.0–15.0] 0.710 1.000 SAS-SMP score 13.0 [7.3–20.0] 15.0 [9.0–22.0] 0.034 0.826 Burnout score 48.5 [25.3–74.0] 50.5 [22.0–79.0] 0.690 1.000 Sex Female 132 (52.8) 137 (54.8) 0.720 1.000 Male 118 (47.2) 113 (45.2) Education level Postgraduate 230 (92.0) 82 (32.8) < 0.001 < 0.001 University 20 (8.0) 87 (34.8) Monthly income High 129 (51.6) 18 (7.2) < 0.001 < 0.001 Disease control Controlled 101 (40.4) 98 (39.2) 0.611 1.000 Polypharmacy (≥ 5 drugs) Yes 44 (17.6) 41 (16.4) 0.542 1.000 Values are median [IQR] or n (%). P values from the Mann–Whitney U test (continuous variables) or χ² test (categorical variables). Bonferroni correction applied at the variable level. The study included 250 physicians and 250 matched non-physician controls (total N = 500). The two groups were comparable in age (median 48 vs 49 years; p = 0.678), number of chronic conditions (median 2 vs 2; p = 0.262), disease duration (median 10 vs 10 years; p = 0.523), and medication burden (median 3 vs 3 medications; p = 0.424). Effect sizes for these comparisons were small (|r| < 0.10), indicating negligible differences. As expected, physicians had significantly higher educational attainment and income levels (p < 0.001 for both). No statistically significant differences were observed in depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), burnout scores, disease control status, or polypharmacy prevalence. Table 2 A. Medication adherence and self-treatment outcomes Outcome Physicians (n = 250) Controls (n = 250) p Adequate adherence (MMAS-8), n (%) 71 (28.4) 78 (31.2) 0.557 MMAS-8 total score 4.0 [2.0–6.0] 4.0 [1.0–6.0] 0.757 ARMS total score 25.0 [18.0–30.0] 23.0 [18.0–29.0] 0.269 GMAS total score 15.0 [8.0–24.0] 16.0 [8.0–23.0] 0.451 Self-treatment (SAS-SMP) 13.0 [7.3–20.0] 15.0 [9.0–22.0] 0.034 Self-reported medication adherence outcomes are presented in Table 2 A. Adequate adherence as assessed by the MMAS-8 was reported by 71 physicians (28.4%) and 78 controls (31.2%), with no statistically significant difference between groups (p = 0.557). Median MMAS-8 total scores were also similar between physicians and controls (4.0 [IQR 2.0–6.0] vs 4.0 [IQR 1.0–6.0]; p = 0.757). Consistent findings were observed across additional adherence measures. Median ARMS scores did not differ significantly between groups (25.0 [IQR 18.0–30.0] in physicians vs 23.0 [IQR 18.0–29.0] in controls; p = 0.269), nor did GMAS scores (15.0 [IQR 8.0–24.0] vs 16.0 [IQR 8.0–23.0]; p = 0.451). Taken together, these results indicate that self-reported adherence patterns were broadly comparable between physicians and matched non-physician controls. With respect to self-treatment practices, controls reported higher SAS-SMP scores compared with physicians (median 15.0 [IQR 9.0–22.0] vs 13.0 [IQR 7.3–20.0]; p = 0.034). Although statistically significant in the unadjusted analysis, this difference did not remain significant after Bonferroni correction (adjusted p = 0.826), suggesting that the magnitude of the difference in self-treatment behavior may be modest. Table 2 B. Objective adherence validation (consenting subset) Outcome Physicians Controls p Consented to pharmacy linkage, n (%) 90 (36.0) 107 (42.8) 0.143 Objective subset size (n) 90 107 — Pharmacy PDC 0.71 [0.57–0.92] 0.71 [0.53–0.86] 0.403 Pill-count adherence 0.68 [0.56–0.78] 0.71 [0.55–0.85] 0.459 Objective adherence (PDC ≥ 0.80), n (%) 34 (37.8) 41 (38.3) 1.000 Agreement MMAS vs PDC (κ) −0.02 0.07 — Correlation MMAS-8 vs PDC (ρ, p) −0.17 (p = 0.12) 0.08 (p = 0.41) — P values are shown where formal statistical comparisons were performed. Agreement (κ) and correlation coefficients are descriptive measures; therefore, no p values are reported for between-group comparisons. A subset of participants consented to pharmacy linkage and pill-count assessment for objective adherence evaluation. Consent was obtained from 90 physicians (36.0%) and 107 controls (42.8%), with no statistically significant difference in consent rates (p = 0.143). Objective adherence measures are summarized in Table 2 B. Median pharmacy refill adherence (PDC) was identical in both groups (0.71), with overlapping interquartile ranges (physicians: 0.71 [0.57–0.92] vs controls: 0.71 [0.53–0.86]; p = 0.403). Pill-count adherence was similarly comparable between physicians and controls (0.68 [0.56–0.78] vs 0.71 [0.55–0.85]; p = 0.459). Adequate objective adherence (PDC ≥ 0.80) was observed in 37.8% of physicians and 38.3% of controls (p = 1.000), indicating no measurable difference between groups in refill-based persistence. Agreement between self-reported adherence (MMAS-8) and objective adherence (PDC) was poor in both groups (κ = −0.02 in physicians and κ = 0.07 in controls). Similarly, correlations between MMAS-8 scores and PDC were weak and non-significant (ρ = −0.17, p = 0.12 among physicians; ρ = 0.08, p = 0.41 among controls). These findings suggest substantial discordance between subjective and objective adherence measures across both physicians and controls. Table 3 Multivariable logistic regression predicting adequate adherence (all participants) Outcome: adequate adherence (yes/no). Complete-case analysis. Predictor Adjusted OR (95% CI) p Physician vs control 1.15 (0.77–1.70) 0.493 Age (per year) 0.99 (0.98–1.01) 0.261 Male sex 0.85 (0.58–1.27) 0.433 Chronic conditions (per + 1) 1.02 (0.81–1.30) 0.841 Disease duration (per year) 0.97 (0.94–1.01) 0.111 Medications (per + 1) 1.26 (1.03–1.54) 0.023 Polypharmacy 0.59 (0.30–1.17) 0.132 PHQ-9 score 1.01 (0.98–1.03) 0.510 GAD-7 score 1.00 (0.96–1.03) 0.763 Self-treatment score 1.03 (1.01–1.05) 0.014 Burnout score 1.00 (0.99–1.01) 0.834 Health insurance 0.97 (0.63–1.49) 0.883 Multivariable logistic regression analysis was performed to identify factors independently associated with adequate adherence (MMAS-8-based) across the full study population (Table 3 ). After adjustment for demographic, clinical, and psychological variables, physician status was not significantly associated with adequate adherence (adjusted OR 1.15, 95% CI 0.77–1.70; p = 0.493). Among the covariates examined, a higher number of prescribed medications was associated with increased odds of adequate adherence (adjusted OR 1.26 per additional medication, 95% CI 1.03–1.54; p = 0.023). In addition, higher self-treatment scores were independently associated with slightly increased odds of adequate adherence (adjusted OR 1.03 per unit increase, 95% CI 1.01–1.05; p = 0.014). Polypharmacy was not significantly associated with adherence (adjusted OR 0.59, 95% CI 0.30–1.17; p = 0.132). No statistically significant associations were observed for age, sex, number of chronic conditions, disease duration, depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), burnout score, or insurance coverage (all p > 0.10). Overall, the multivariable analysis suggests that adherence behavior was not determined by professional status but was more closely associated with medication-related burden and self-treatment behavior. Table 4 Physician-only predictors of adequate adherence Predictor Adjusted OR (95% CI) p Age (per year) 0.99 (0.97–1.01) 0.454 Male sex 0.74 (0.41–1.35) 0.329 Medications (per + 1) 1.38 (1.01–1.88) 0.041 Self-treatment score 1.04 (1.00–1.08) 0.031 Weekly work hours 1.00 (0.98–1.03) 0.728 Night shifts/month 0.99 (0.86–1.12) 0.827 Burnout score 1.00 (0.99–1.01) 0.648 Specialty (surgical vs medical) 0.89 (0.44–1.79) 0.740 A separate multivariable model was constructed among physicians only to explore occupational and behavioral predictors of adherence (Table 4 ). In this model, a higher number of prescribed medications was significantly associated with adequate adherence (adjusted OR 1.38 per additional medication; 95% CI, 1.01–1.88; p = 0.041). The self-treatment score was also significantly associated with adherence (adjusted OR = 1.04, 95% CI = 1.00–1.08; p = 0.031). Occupational characteristics, including weekly working hours (p = 0.728), number of night shifts per month (p = 0.827), and specialty type (surgical vs medical; p = 0.740), were not significantly associated with adherence. Burnout was also not significantly associated with adherence within the physician subgroup (p = 0.648). Discussion The current study found no significant difference in medication adherence between physicians with chronic illness and matched non-physician controls, based on both self-reported and objective adherence measures. This lack of a large group effect implies that medical knowledge alone is not a guarantee of optimal adherence behavior. Instead, adherence appears to reflect a broader behavioral and psychological process that is both motivated and constrained by self-management styles and situational impediments, to which even healthcare workers are not exempt. High medical literacy may not eliminate specific psychological barriers to adherence among physicians. Clinicians may minimize chronic illness or put off suitable self-care due to symptom normalization, fear of being perceived as vulnerable, and worries about professional stigma. Furthermore, role conflict as both a caregiver and a patient can undermine consistent adherence to treatment routines, a common cause of adherence issues observed among other non-physician groups. There was no significant difference between the groups in medication adherence, as measured by three self-report measures (MMAS-8, ARMS, GMAS) and by objective measures (pharmacy PDC and pill count), in the consenting group in this cross-sectional comparison of physicians with chronic illness versus matched non-physician controls. This difference in the null group is significant because the physicians were more educated and earned more, yet behavioral adherence was comparable. The observation supports modern adherence models, which conceptualize medication taking as a behavioral process influenced by multidimensional determinants such as opportunity, capability, and motivation, rather than by health knowledge alone [ 9 ]. Self-treatment behavior in this context may not necessarily reflect neglect or irresponsibility, but instead a form of perceived control and patient agency. Participants who engage in self-adjustment or self-management may feel more autonomy over their treatment, which can paradoxically support persistence and routine medication taking. However, such behaviors may also carry clinical risks if undertaken without structured medical supervision, highlighting the importance of guided self-management. From a behavioral perspective, these findings can be interpreted using established psychological frameworks such as the Health Belief Model and self-regulation theory. Even when perceived knowledge is high, adherence depends on perceived susceptibility, perceived necessity of treatment, and the ability to translate intentions into sustained daily behavior. This behavioral–cognitive gap may explain why physicians do not consistently demonstrate better adherence than non-physician patients. Given that physicians may be expected to be more non-adherent due to greater medical knowledge and easier access to health services, our data indicate that professional identity and training do not necessarily lead to improved daily practice. One possible explanation is that physicians face specific obstacles that offset the benefits of their work (time constraints, conflicting interests, symptom normalization, non-continuous self-care). Health services research evidence from the pandemic era demonstrates significant disruptions to usual healthcare use and engagement in chronic care among the general population, which may affect physicians and non-physicians and could compress adherence disparities [ 10 , 11 ]. Additionally, delays in seeking healthcare have been reported among healthcare workers during illness, indicating that professionals do not consistently adhere to optimal self-care practices [ 12 ]. One of the most important contributions of this study is the finding of poor concordance between self-reported and objective adherence. In our objective subset, there was almost no agreement between MMAS-8 and PDC (k ≥ 0), and the correlations were weak and not statistically significant. This is consistent with recent literature on adherence measurement, which suggests that self-report measures, although convenient, may be limited by recall bias and social desirability and may not accurately reflect the implementation and persistence of interventions. A systematic review of patient-reported outcome measures of medication non-adherence highlights wide variability in the types of instruments used to measure intentional or unintentional non-adherence and heterogeneity in psychometric properties across tools [ 9 ]. Similarly, a systematic review of self-reported primary care tools emphasizes feasibility but highlights measurement limitations and underscores the importance of careful selection and triangulation [ 13 ]. Empirical research comparing subjective and refill-based adherence is also reflected in our findings and typically indicates only moderate agreement, even when associations are present [ 14 ]. Methodologically, objective measures are not gold standards, but they enhance inference by measuring behavior beyond perception. PDC is popular due to its scalability and its reliance on dispensing data, which is influenced by choices related to the analysis (e.g., definitions of the nominator and assumptions about refill time). The methodological study conducted in 2021 illustrates how various PDC algorithms and assumptions can significantly alter adherence estimates, underscoring the importance of explicit operational definitions [ 15 ]. Simultaneously, studies on the optimal cut-offs for applying PDC/MPR in type 2 diabetes indicate that thresholds (typically 0.80) have clinical significance but are not necessarily optimal in other settings and outcomes, which can inform our PDC ≥ 0.80 classification [ 16 ]. Collectively, the literature on measurement supports our decision to report continuous adherence distributions (median PDC) and categorical cutoffs, and it places our decision to report discordance with self-report in context. Among participants who agreed to pharmacy linkage, there were no significant differences in median PDC between physicians and controls, Pill-count adherence, or the percentage who achieved the PDC > 0.80 threshold. This supports the conclusion that the lack of between-group differences is not a self-report artifact. The observation that inconsistencies between self-report and indirect measures are not unusual is supported by evidence from other disease areas [ 17 ]. Accordingly, in our context, the objective subset serves as an internal validation check. Among more adherent physicians, we would expect directional separation in PDC or pill count, but this was not the case. Control conditions had higher self-treatment scores than physicians, suggesting that controls were more engaged in self-directed medication behaviors than physicians. Modern data have indicated that self-medication is common in adults with chronic illnesses and is commonly driven by barriers to access and convenience, as well as a negative evaluation of symptoms, as opposed to an easy ignoring of medical guidance [ 18 ]. There was a positive association between higher self-treatment scores and greater odds of achieving adequate adherence in our multivariate models (MMAS-8-based). One implication is that self-treatment behavior can be associated with active self-management and high engagement (e.g., purposely planning regimens, symptom-based adjustments) that co-exist with routine adherence- although it does not mean that it is appropriate or safe. The broader evidence on self-management interventions indicates that patient agency or systematic self-management assistance can lead to better outcomes and self-efficacy, yet only with heavy reliance on guidance, follow-up, and system supports [ 19 ]. Adequate adherence was independently associated with a greater number of medications in both the overall and physician-only models. Although polypharmacy is commonly associated with regimen complexity and the risk of nonadherence, recent studies indicate that it is heterogeneous and context-dependent, shaped by support structures and the suitability of medications for a patient. A comprehensive, synthesized, up-to-date review of interventions to address polypharmacy indicates that drug reviews and deprescribing interventions may enhance process (appropriateness) but not clinical outcomes; this highlights that polypharmacy is a disease burden as well as a care intensity, not merely too many pills. [ 20 ]. Polypharmacy stewardship concept further states that the management of multimorbidity needs to be considered by coordinated actions that should take into account drug-drug interactions, omissions, and individual objectives, once more, stating that the number of medications is not the entire story [ 20 ]. At the individual level, patients with higher medication burden may exhibit greater adherence because the illness becomes more salient, patients receive more frequent follow-ups, or routines are more organized. The latest research on polypharmacy has identified drug- and patient-related correlates of adherence, including control intensity and regimen management capability [ 21 ]. The given interpretation is consistent with our baseline comparability: the number of medications and polypharmacy showed similar medians and prevalence in both groups, and the relationship between medications and adherence is more a consequence of behavior and engagement in the shared burden than a disease-severity confounding factor. We were unable to identify independent relationships between PHQ-9, GAD-7, burnout, or (in the case of physicians) workload measures and adherence. This null result must be interpreted with caution: mental health can alter adherence through pathways such as motivation, executive function, and a perceived necessity to take medication; however, when multiple determinants are concurrent, and adherence is self-reported, effects may be diluted. Measurement studies of self-report measures indicate that they may assess different dimensions of adherence than objective measures and may fail to capture certain psychological drivers in certain situations [ 9 ]. Furthermore, recent system-level changes in healthcare use and service delivery, such as reduced digital care, may have led individuals to continue taking medications in ways that generic workload metrics have not fully predicted [ 10 , 21 ]. We found that a population-agnostic method of assessing adherence and intervention is more effective: clinicians cannot assume that physicians are always adherent, and researchers cannot afford to use self-report measures when high accuracy is needed. There is growing literature on measurement suggesting multimethod assessment (self-report plus pharmacy/refill or other objective indicators), as different instruments identify different stages and mechanisms of adherence. [ 9 , 15 ]. Treatments must also be multi-dimensional. Evidence syntheses indicate that adherence-support interventions that are implemented in the context of complex chronic illnesses, in most cases, involve the use of combinations (education, follow-up, reminders, structured self-management support, and care coordination) [ 21 , 23 ]. Digital tools can be beneficial, as they facilitate routines, access, and engagement; however, their effectiveness depends on their implementation and patients' circumstances [ 24 , 25 ]. Clinical implications Clinicians should not assume that physicians are inherently adherent to long-term therapy. Physicians living with chronic illness may require the same adherence screening and follow-up support as other patient groups. Educational knowledge alone is insufficient; behavioral and psychological interventions may be needed. Structured self-management support and stigma-free access to care should be promoted for healthcare professionals. Study limitations Several limitations should be considered when interpreting the findings of this study. First, the cross-sectional design precludes any inference regarding causality or temporal directionality. Associations observed between self-treatment behavior, medication burden, and adherence cannot establish whether these factors precede, follow, or co-develop with adherence patterns. Reverse causation remains plausible; for example, individuals who are generally more engaged in their care may both adhere more consistently and report greater self-directed medication practices. Second, adherence was primarily assessed using self-report instruments. Although validated tools were employed, self-reported adherence is susceptible to recall bias and social desirability bias. This concern may be particularly relevant among physicians, whose professional identity could influence reporting patterns. Although objective adherence measures (PDC and pill-count data) were included in a subset of participants, these methods also serve as indirect proxies of medication-taking behavior and do not confirm actual ingestion. Third, objective adherence analysis was limited to participants who consented to pharmacy linkage, representing approximately one-third of physicians and slightly over two-fifths of controls. This introduces the possibility of selection bias, as individuals willing to provide pharmacy data may differ systematically from non-consenters in unmeasured ways (e.g., trust in healthcare systems, privacy concerns, or adherence behavior itself). Therefore, objective adherence findings should be interpreted as internally informative but not necessarily generalizable to the entire cohort. Fourth, the SAS-SMP instrument measures a broad spectrum of self-directed medication behaviors without differentiating between structured self-management, informal dose adjustments, and potentially unsafe self-medication practices. As such, higher scores may reflect patient autonomy and engagement, access-related adaptations, or clinically suboptimal behaviors. The instrument does not distinguish between beneficial and potentially harmful self-treatment practices, thereby limiting interpretive precision. Future research using more granular assessments of self-management behaviors would help clarify these distinctions. Fifth, although physicians and controls were matched by age, sex, and disease category, substantial differences in socioeconomic indicators (education and income) remained. At the same time, these variables were considered in multivariable analyses where applicable; residual confounding cannot be excluded. Additional unmeasured factors—such as health literacy, self-efficacy, illness perceptions, healthcare continuity, or medication class–specific characteristics—may have influenced adherence behavior. Sixth, disease severity and clinical control were assessed using available clinical indicators; however, these may not fully capture the complexity of multimorbidity or treatment burden. Detailed medication-class–specific adherence patterns were not analyzed, and heterogeneity across chronic conditions may have attenuated condition-specific effects. Finally, the study was conducted within a single institutional setting in Upper Egypt, which may limit generalizability to other healthcare systems, cultural contexts, or physician populations with different practice environments. Conclusion In this cross-sectional comparative study, medication adherence among physicians with chronic illness was broadly similar to that of matched non-physician controls, as assessed by both self-reported and objective measures. After adjustment for demographic, clinical, and psychological variables, professional status was not independently associated with adequate adherence. Adherence behavior appeared more closely associated with treatment-related and behavioral factors—particularly medication burden and self-reported self-treatment practices—than with occupational characteristics or professional background. However, the observed association between self-treatment behavior and adherence should be interpreted cautiously, as the self-treatment measure encompasses heterogeneous practices that may reflect patient autonomy, access-related adaptations, or potentially unsafe medication behaviors. The limited concordance between self-reported and objective adherence measures further underscores the complexity of adherence assessment and highlights the importance of multimodal measurement approaches in future research. Given the cross-sectional design and potential residual confounding, these findings should be regarded as hypothesis-generating. Longitudinal studies employing finer-grained assessments of self-management behavior and condition-specific adherence patterns are needed to clarify the mechanisms underlying medication-taking behavior among physicians and other patient populations with chronic illness. Abbreviations MMAS-8 8-item Morisky Medication Adherence Scale ARMS Adherence to Refills and Medications Scale GMAS General Medication Adherence Scale SAS-SMP Self-Administration Scale for Self-Medication Practices PHQ-9 Patient Health Questionnaire-9 GAD-7 Generalized Anxiety Disorder-7 PDC Proportion of Days Covered IRB Institutional Review Board Declarations Ethics approval and consent to participate The research was carried out in accordance with the ethical principles of the Declaration of Helsinki. Luxmed-070126-108 was an IRB local approval accepted and applied by the Research Ethics Committee of the Faculty of Medicine, Luxor University, Luxor, Egypt (date of approval: 07 January 2026). Informed consent was obtained in writing, and all participants provided written consent before participating in the study. Consent for publication Not applicable. In this manuscript, no personal data of a person in any form, including identifiable information, photographs, or video records, is given. Availability of data and materials The datasets produced and/or measured in the present study cannot be made publicly available for ethical and privacy reasons; nevertheless, they can be retrieved by the respective author upon reasonable request and in accordance with the Faculty of Medicine, Luxor University. Funding This study was not funded by any specific grant, whether in the public, commercial, or not-for-profit sectors. Competing interests The authors declare that they have no competing financial or non-financial interests related to this work. Authors’ contributions A.J.N.A. contributed to study conception and design, data collection, and manuscript drafting. M.A.B. contributed to data analysis, interpretation of results, and critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank all participants for their time and cooperation. The authors also acknowledge the Faculty of Medicine, Luxor University, for facilitating the ethical approval and conduct of this study. Authors’ information Ahmed Jado Nabih Ali and Mahmoud Ahmed Bekiet are lecturers at the Faculty of Medicine, Luxor University, Luxor, Egypt. References Burnier M. The role of adherence in patients with chronic diseases. Eur J Intern Med. 2024;119:1–5. Schnorrerova P, Matalova P, Wawruch M. Medication Adherence and Intervention Strategies: Why Should We Care. Bratislava Med J 2025 Jun 10:1–1. Dunbar-Jacob J, Zhao J. Medication Adherence Measurement in Chronic Diseases: A State-of-the-Art Review of the Literature. Nurs Rep. 2025;15(10):370. Oliveira HC, Hayashi D, Carvalho SD, Barros RD, Neves ML, Andrechuk CR, Alexandre NM, Ribeiro PA, Rodrigues RC. Quality of measurement properties of medication adherence instruments in cardiovascular diseases and type 2 diabetes mellitus: a systematic review and meta-analysis. Syst reviews. 2023;12(1):222. Shanafelt TD, West CP, Dyrbye LN, Trockel M, Tutty M, Wang H, Carlasare LE, Sinsky C. Changes in burnout and satisfaction with work-life integration in physicians during the first 2 years of the COVID-19 pandemic. InMayo Clinic Proceedings 2022 Dec 1 (Vol. 97, No. 12, pp. 2248–2258). Elsevier. Baracaldo-Santamaría D, Trujillo-Moreno MJ, Pérez-Acosta AM, Feliciano-Alfonso JE, Calderon-Ospina CA, Soler F. Definition of self-medication: a scoping review. Therapeutic Adv drug Saf. 2022;13:20420986221127501. Zheng Y, Liu J, Tang PK, Hu H, Ung CO. A systematic review of self-medication practice during the COVID-19 pandemic: implications for pharmacy practice in supporting public health measures. Front Public Health. 2023;11:1184882. Zhang J, Hu J, Zheng Y, Dai T. Factors affecting medication adherence in patients with chronic diseases: A systematic literature review. Chin Gen Pract J. 2025;2(3):100072. Fahrni ML, Saman KM, Alkhoshaiban AS, Naimat F, Ramzan F, Isa KA. Patient-reported outcome measures to detect intentional, mixed, or unintentional non-adherence to medication: a systematic review. BMJ Open. 2022;12(9):e057868. Moynihan R, Sanders S, Michaleff ZA, Scott AM, Clark J, To EJ, Jones M, Kitchener E, Fox M, Johansson M, Lang E. Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review. BMJ Open. 2021;11(3):e045343. Gertz AH, Pollack CC, Schultheiss MD, Brownstein JS. Delayed medical care and underlying health in the United States during the COVID-19 pandemic: a cross-sectional study. Prev Med Rep. 2022;28:101882. de Wilton A, Kilich E, Chaudhry Z, Bell LC, Gahir J, Cadman J, Lever RA, Logan SA. Delayed healthcare seeking and prolonged illness in healthcare workers during the COVID-19 pandemic: a single-centre observational study. BMJ open. 2020;10(11):e040216. Rickles NM, Mulrooney M, Sobieraj D, Hernandez AV, Manzey LL, Gouveia-Pisano JA, Townsend KA, Luder H, Cappelleri JC, Possidente CJ. A systematic review of primary care-focused, self-reported medication adherence tools. J Am Pharmacists Association. 2023;63(2):477–90. Murali KM, Mullan J, Roodenrys S, Cheikh Hassan HI, Lonergan MA. Exploring the agreement between self-reported medication adherence and pharmacy refill-based measures in patients with kidney disease. Patient preference adherence 2022 Dec 31:3465–77. Prieto-Merino D, Mulick A, Armstrong C, Hoult H, Fawcett S, Eliasson L, Clifford S. Estimating proportion of days covered (PDC) using real-world online medicine suppliers’ datasets. J Pharm Policy Pract. 2021;14(1):113. Lim MT, Ab Rahman N, Teh XR, Chan CL, Thevendran S, Ahmad Hamdi N, Lim KK, Sivasampu S. Optimal cut-off points for adherence measure among patients with type 2 diabetes in primary care clinics: a retrospective analysis. Therapeutic Adv Chronic Disease. 2021;12:2040622321990264. Liao R, Tang Z, Zhang N, Hu L, Chang Z, Ren J, Bai X, Shi J, Fan S, Pei R, Du L. Discrepancies between self-reported medication in adherence and indirect measurement adherence among patients undergoing antiretroviral therapy: a systematic review. Infect Dis Poverty. 2024;13(04):1–3. Alwhaibi M, Malik SB, Alswailem L, Alruthia Y. Self-medication among adults with chronic health conditions: a population-based cross-sectional survey in Saudi Arabia. BMJ open. 2023;13(4):e069206. Niño de Guzmán Quispe E, Martínez García L, Orrego Villagrán C, Heijmans M, Sunol R, Fraile-Navarro D, Pérez-Bracchiglione J, Ninov L, Salas-Gama K, Viteri García A, Alonso-Coello P. The perspectives of patients with chronic diseases and their caregivers on self-management interventions: a scoping review of reviews. Patient-Patient-Centered Outcomes Res. 2021;14(6):719–40. Keller MS, Qureshi N, Mays AM, Sarkisian CA, Pevnick JM. Cumulative update of a systematic overview evaluating interventions addressing polypharmacy. JAMA Netw Open. 2024;7(1):e2350963. Liu J, Yu Y, Yan S, Zeng Y, Su S, He T, Wang Z, Ding Q, Zhang R, Li W, Wang X. Risk factors for self-reported medication adherence in community-dwelling older patients with multimorbidity and polypharmacy: a multicenter cross-sectional study. BMC Geriatr. 2023;23(1):75. Lammila-Escalera E, Greenfield G, Pan Z, Nicholls D, Majeed A, Hayhoe BW. A systematic review of interventions to improve medication adherence in adults with mental-physical multimorbidity in primary care. Br J Gen Pract. 2024 Mar 1. Berardinelli D, Conti A, Hasnaoui A, Casabona E, Martin B, Campagna S, Dimonte V. Nurse-led interventions for improving medication adherence in chronic diseases: A systematic review. InHealthcare 2024 Nov 22 (Vol. 12, No. 23, p. 2337). MDPI. Brands MR, Gouw SC, Beestrum M, Cronin RM, Fijnvandraat K, Badawy SM. Patient-centered digital health records and their effects on health outcomes: systematic review. J Med Internet Res. 2022;24(12):e43086. Quach S, Michaelchuk W, Benoit A, Oliveira A, Packham TL, Goldstein R, Brooks D. Mobile health applications for self-management in chronic lung disease: a systematic review. Netw Model Anal Health Inf Bioinf. 2023;12(1):25. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8888407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608334704,"identity":"08c9ba06-6ddc-4044-a4f2-d638b336e5b2","order_by":0,"name":"Mahomoud Ahmed Bekiet","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABc0lEQVRIie3SPUvDQBgH8EcOrsvVrict6Vc4CURLS/NVEgLpErRT6KSFQCbBNYLoV4iLcyTYLlrXlDi0S6cOcVC6SL3LC6UqiJtI/ku4y/1yzz0XgDJl/mBYQMQDAabA0KoPBCooADHE4sUsX0c3pFWQmiDAQAKEtQ3RvhK1IHteRmRAhEG2/nuijB/v/Re3DSx2fEH0c4e8+ol92NytOwHobigdDNFdTKDTHWbk4cicXrg9YM+jPko48cLq7dSb0H23MdI4MeVGgI02AdPISWApcdUNgUVWWpg+5ITPUA1Tiw/dju4BUeoEQiPv2NMyI2pOTq9DsojfU3KccEI5qb1xsi5I9HkXPyQ43sl2gWIXzEnQLchS4ZX3SHoWQW5CrEzPxFmoyQJtYsoUYbl1yQytKMxSosRuS2nHVgPQr8bOIlrZJ82mZ8xniR1KtOLMo+Wgq27/BeJ2KiuA9fZ0ANmtiY/zzsBvo/68pEyZMmX+ZT4AEoqKs5PJeT8AAAAASUVORK5CYII=","orcid":"","institution":"Luxor University","correspondingAuthor":true,"prefix":"","firstName":"Mahomoud","middleName":"Ahmed","lastName":"Bekiet","suffix":""},{"id":608334706,"identity":"2444448a-51c9-4c6d-99d7-b6e448b5c5e3","order_by":1,"name":"Ahmed Jado Nabih Ali","email":"","orcid":"","institution":"Luxor University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Jado Nabih","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2026-02-15 21:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8888407/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8888407/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564115,"identity":"afb75ec8-f9f1-4a7a-a9a8-28b0988af083","added_by":"auto","created_at":"2026-03-27 12:48:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":962451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8888407/v1/a9289e14-cdf6-4014-a8eb-93e6750930e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Medical Knowledge Is Not Enough: Medication Adherence and Self- Treatment Among Physicians With Chronic Illness in Upper Egypt","fulltext":[{"header":"Background","content":"\u003cp\u003eMedication adherence has become a fundamental pillar of the management of chronic diseases. Still, suboptimal adherence has become the norm in most conditions and health care systems, negatively impacting treatment outcomes and leading to preventable morbidity, health care utilization, and expenditure. According to recent reviews, adherence is not only a behavioral phenomenon but also a dynamic process shaped by patient, therapy, and system-level factors, and it remains a challenge despite the availability of effective therapeutic interventions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent syntheses indicate that studies on adherence are complicated by varying definitions and measurement methods, which can yield different estimates and make results and situations non-comparable [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the same time, physician health is becoming a prominent focus in the post-2020 period, and physicians are exposed to persistent occupational stressors that may affect self-care behaviors. According to the national survey work conducted on a large scale during the COVID-19 period, there were notable changes in physician burnout and work-life integration, which implies that there is a work environment that can potentially result in some deterioration of routine preventive care, follow-up persistence, and maintenance of health behaviors over an extended period [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Physicians are highly health-literate and have favorable access to medical knowledge, which does not always translate into optimal individual health behaviors. Instead, time limitations, symptom normalization, professional stigma, and fragmented care paths may pose specific setbacks to continuous self-management among physicians with chronic disease themselves.\u003c/p\u003e \u003cp\u003eThe issue of self-treatment and self-medication is particularly applicable when clinicians themselves or they become patients. The new methodological literature underscores that self-medication is not consistently defined across research contexts, making it more difficult to interpret and compare its rates and determinants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Contemporary systematic research on self-medication during the pandemic indicates substantial differences in prevalence and determinants (e.g., convenience, perceived mild symptoms, barriers to access). Medications prescribed to the population are heterogeneous: analgesics, antibiotics, supplements, and other treatments that can be obtained both in pharmacies and informally [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These trends raise questions about the safety and efficacy of medications, interactions, disease progression, masking, and, more importantly, in chronic conditions, the possible replacement of well-structured longitudinal care with ad hoc care.\u003c/p\u003e \u003cp\u003eThe other problem is that the approach is critical regarding adherence measurement. The latest literature review of adherence measurement in chronic disease studies indicates that self-report measures remain widely used due to their practicality and affordability. In contrast, objective measures, such as pharmacy refill records, are more accurate but are limited by logistical and connectivity issues [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On the same note, a COSMIN-informed systematic review (cardiovascular disease and type 2 diabetes) highlights inconsistent measurement quality of widely used patient-reported adherence measures and recommends paying close attention to instrument selection and reporting [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These problems are particularly acute in groups such as physicians, where social desirability and professional identity may bias self-reports, and objective validation would be beneficial in such groups.\u003c/p\u003e \u003cp\u003eAlthough physician well-being and clinical outcomes of non-adherence are important, direct evidence comparing medication adherence among physicians with chronic illness using matched non-physician controls is limited [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This study aimed to explore medication adherence behavior among physicians living with chronic illness compared with matched non-physician controls, and to examine the psychological and behavioral factors associated with adherence, including self-treatment practices.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Reporting Framework\u003c/h2\u003e \u003cp\u003eThis study was a cross-sectional comparative observational study conducted in Upper Egypt to evaluate medication adherence and self-treatment behaviors among physicians living with chronic illnesses, compared with non-physician controls. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies. The primary objective was to compare medication adherence between physicians and non-physician patients. In contrast, secondary objectives included identifying behavioral and psychological factors associated with adherence and assessing concordance between self-reported adherence and objective adherence measures in a consenting subset.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Setting\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited from outpatient clinics affiliated with the Faculty of Medicine, Luxor University, Egypt. Recruitment was conducted through routine outpatient visits to ensure that both physicians and non-physician controls were drawn from similar healthcare environments and had comparable access to outpatient services. Data collection occurred following institutional ethical approval granted in January 2026. The outpatient clinic setting included internal medicine and specialty chronic disease follow-up services, reflecting typical clinical pathways for individuals requiring long-term pharmacotherapy.\u003c/p\u003e\n\u003ch3\u003eParticipants and Eligibility Criteria\u003c/h3\u003e\n\u003cp\u003eEligible participants were adult individuals (\u0026ge;\u0026thinsp;18 years) diagnosed with at least one chronic illness requiring long-term pharmacological treatment. The physician group consisted of practicing physicians with documented chronic illness receiving ongoing pharmacotherapy. The control group consisted of non-physician adult patients with chronic illnesses recruited from the same outpatient clinical settings. Participants were excluded if they were unable to provide informed consent, had acute medical instability requiring urgent intervention, or had incomplete adherence data that prevented classification of adherence status.\u003c/p\u003e \u003cp\u003eThe control group was selected using a matching strategy based on age, sex, and disease category. Matching was performed using a 1:1 frequency-matching approach, such that the distribution of these characteristics in the control group approximated that in the physician group. Disease category matching was applied to minimize confounding related to the clinical type of chronic illness and associated medication regimens. Both groups were recruited from the same clinical settings to reduce systematic differences in healthcare access and follow-up intensity.\u003c/p\u003e\n\u003ch3\u003eSample Size Estimation\u003c/h3\u003e\n\u003cp\u003eThe target sample size was determined based on the minimum number of participants required to detect a clinically meaningful difference in the prevalence of adequate medication adherence between physicians and controls, assuming a two-sided alpha level of 0.05. Based on preliminary assumptions, a minimum of 178 participants per group was estimated to provide adequate statistical power. To account for incomplete responses, missing questionnaire data, and potential exclusions, the planned sample size was increased to 250 participants per group, yielding a total sample of 500 participants.\u003c/p\u003e\n\u003ch3\u003eData Collection Procedures\u003c/h3\u003e\n\u003cp\u003eData were collected using structured questionnaires and clinical record extraction after ethical approval. Participants completed standardized study questionnaires assessing adherence, psychological symptoms, burnout, and self-treatment behavior. Sociodemographic and clinical characteristics, including disease duration, number of chronic conditions, and number of prescribed medications, were collected via participant report and, when available, outpatient medical records. Disease control status was recorded based on documentation in outpatient follow-up records as noted by the treating clinician. Data were anonymized and coded before analysis to ensure confidentiality, particularly in the physician cohort, where privacy concerns could influence reporting.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExposure Definition (Physician Status)\u003c/h2\u003e \u003cp\u003eThe main exposure variable was professional status (physician vs non-physician control). Physicians were defined as individuals with a medical degree who were actively engaged in clinical practice and receiving pharmacotherapy for chronic illness. Controls were defined as adult non-physician patients attending the same outpatient services with comparable chronic illness categories and requiring long-term medication use. This exposure variable was treated as the primary grouping variable in comparative analyses and was also included as a covariate in multivariable regression modeling.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcomes: Medication Adherence Assessment\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was medication adherence, assessed using the 8-item Morisky Medication Adherence Scale (MMAS-8), a validated self-report instrument widely used in chronic disease populations. MMAS-8 scores were calculated according to established scoring guidelines, and adherence was categorized using standard cutoffs to define adequate versus inadequate adherence. Secondary adherence outcomes included adherence estimates from additional validated instruments, such as the Adherence to Refills and Medications Scale (ARMS) and the General Medication Adherence Scale (GMAS), which were included to enhance the robustness of adherence assessment across different adherence constructs.\u003c/p\u003e \u003cp\u003eIn a consenting subset of participants, objective adherence was assessed using pharmacy refill data and pill-count documentation. Pharmacy refill adherence was quantified using the proportion of days covered (PDC), calculated as the number of days during which medication was available divided by the total observation period. A PDC threshold of \u0026ge;\u0026thinsp;0.80 was used to define adequate objective adherence, consistent with commonly applied pharmacoepidemiologic standards. Pill-count adherence was calculated from documented pill-count records when available. Concordance between subjective adherence (MMAS-8) and objective adherence (PDC) was assessed using Cohen\u0026rsquo;s kappa statistic and correlation analysis.\u003c/p\u003e\n\u003ch3\u003eAssessment of Self-Treatment and Psychological Variables\u003c/h3\u003e\n\u003cp\u003eSelf-treatment behavior was measured using the Self-Administration Scale for Self-Medication Practices (SAS-SMP), a structured tool designed to quantify the extent of self-directed medication behaviors. Higher scores indicated greater engagement in self-treatment practices, including medication adjustment or self-management without formal clinical supervision.\u003c/p\u003e \u003cp\u003ePsychological symptoms were evaluated using validated symptom scales. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), and anxiety symptoms were assessed using the Generalized Anxiety Disorder-7 (GAD-7). Burnout was measured using a standardized burnout scale, and total burnout scores were included in analyses. These psychological variables were incorporated as potential predictors and confounders, given established associations between psychological distress, self-management capacity, and adherence behavior.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCovariates and Potential Confounding Factors\u003c/h2\u003e \u003cp\u003ePrespecified covariates included age, sex, number of chronic conditions, disease duration, number of prescribed medications, and polypharmacy status (defined as \u0026ge;\u0026thinsp;5 medications). Socioeconomic variables, including education level and monthly income, were recorded as descriptive characteristics given expected differences between physicians and controls. Health insurance status was also included as a covariate due to its potential association with medication access and refill continuity. The inclusion of these covariates was based on clinical plausibility and evidence from adherence literature suggesting that demographic, treatment-related, and psychological factors influence adherence outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as medians and interquartile ranges because of non-normal distributions and compared between groups using the Mann\u0026ndash;Whitney U test. Categorical variables were presented as frequencies and percentages and compared using chi-square tests or Fisher\u0026rsquo;s exact test where expected cell counts were small. A Bonferroni correction was applied to multiple comparisons of baseline characteristics to reduce the likelihood of a type I error.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression was performed to identify independent predictors of adequate adherence, with adherence status (adequate vs inadequate) as the dependent variable. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were reported. Variables entered into the regression model were selected based on clinical relevance and literature-informed plausibility rather than automated statistical selection procedures. A separate physician-only multivariable regression model was conducted to examine whether occupational variables, including weekly working hours, number of night shifts per month, and specialty type, were associated with adherence. Complete-case analysis was used for regression modeling, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Agreement between subjective and objective adherence measures was evaluated using Cohen\u0026rsquo;s kappa statistic, and correlations between MMAS-8 scores and PDC values were assessed using Spearman's rank correlation coefficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Research Ethics Committee of the Faculty of Medicine, Luxor University, Egypt (IRB approval number: Luxmed-070126-108; approval date: 07 January 2026). All participants provided written informed consent prior to inclusion. Participants were informed about the voluntary nature of participation, confidentiality measures, and their right to withdraw at any time without affecting clinical care. For the objective adherence assessment, additional consent was obtained for pharmacy refill data linkage. All study data were anonymized and stored securely, with access limited to the research team.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUse of Artificial Intelligence Tools\u003c/h2\u003e \u003cp\u003eGrammarly was used for language editing and improvement of manuscript clarity. No artificial intelligence tools were used for data generation, statistical analysis, interpretation of findings, or decision-making regarding study conclusions. The authors retain full responsibility for the integrity and content of the manuscript.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of physicians and controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysicians (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep (Bonferroni)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.0 [35.0\u0026ndash;59.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.0 [38.0\u0026ndash;57.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic conditions (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0 [1.0\u0026ndash;3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0 [1.0\u0026ndash;3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0 [5.0\u0026ndash;14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0 [5.0\u0026ndash;16.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0 [2.0\u0026ndash;4.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0 [2.0\u0026ndash;4.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0 [7.0\u0026ndash;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0 [7.3\u0026ndash;21.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0 [4.0\u0026ndash;16.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0 [4.0\u0026ndash;15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS-SMP score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0 [7.3\u0026ndash;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.0 [9.0\u0026ndash;22.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.5 [25.3\u0026ndash;74.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.5 [22.0\u0026ndash;79.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy (\u0026ge;\u0026thinsp;5 drugs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eValues are median [IQR] or n (%). P values from the Mann\u0026ndash;Whitney U test (continuous variables) or χ\u0026sup2; test (categorical variables). Bonferroni correction applied at the variable level.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe study included 250 physicians and 250 matched non-physician controls (total N\u0026thinsp;=\u0026thinsp;500). The two groups were comparable in age (median 48 vs 49 years; p\u0026thinsp;=\u0026thinsp;0.678), number of chronic conditions (median 2 vs 2; p\u0026thinsp;=\u0026thinsp;0.262), disease duration (median 10 vs 10 years; p\u0026thinsp;=\u0026thinsp;0.523), and medication burden (median 3 vs 3 medications; p\u0026thinsp;=\u0026thinsp;0.424). Effect sizes for these comparisons were small (|r| \u0026lt; 0.10), indicating negligible differences.\u003c/p\u003e \u003cp\u003eAs expected, physicians had significantly higher educational attainment and income levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). No statistically significant differences were observed in depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), burnout scores, disease control status, or polypharmacy prevalence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA. Medication adherence and self-treatment outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysicians (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate adherence (MMAS-8), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMAS-8 total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 [2.0\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0 [1.0\u0026ndash;6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARMS total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.0 [18.0\u0026ndash;30.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.0 [18.0\u0026ndash;29.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGMAS total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 [8.0\u0026ndash;24.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0 [8.0\u0026ndash;23.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-treatment (SAS-SMP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.0 [7.3\u0026ndash;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0 [9.0\u0026ndash;22.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSelf-reported medication adherence outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. Adequate adherence as assessed by the MMAS-8 was reported by 71 physicians (28.4%) and 78 controls (31.2%), with no statistically significant difference between groups (p\u0026thinsp;=\u0026thinsp;0.557). Median MMAS-8 total scores were also similar between physicians and controls (4.0 [IQR 2.0\u0026ndash;6.0] vs 4.0 [IQR 1.0\u0026ndash;6.0]; p\u0026thinsp;=\u0026thinsp;0.757).\u003c/p\u003e \u003cp\u003eConsistent findings were observed across additional adherence measures. Median ARMS scores did not differ significantly between groups (25.0 [IQR 18.0\u0026ndash;30.0] in physicians vs 23.0 [IQR 18.0\u0026ndash;29.0] in controls; p\u0026thinsp;=\u0026thinsp;0.269), nor did GMAS scores (15.0 [IQR 8.0\u0026ndash;24.0] vs 16.0 [IQR 8.0\u0026ndash;23.0]; p\u0026thinsp;=\u0026thinsp;0.451). Taken together, these results indicate that self-reported adherence patterns were broadly comparable between physicians and matched non-physician controls.\u003c/p\u003e \u003cp\u003eWith respect to self-treatment practices, controls reported higher SAS-SMP scores compared with physicians (median 15.0 [IQR 9.0\u0026ndash;22.0] vs 13.0 [IQR 7.3\u0026ndash;20.0]; p\u0026thinsp;=\u0026thinsp;0.034). Although statistically significant in the unadjusted analysis, this difference did not remain significant after Bonferroni correction (adjusted p\u0026thinsp;=\u0026thinsp;0.826), suggesting that the magnitude of the difference in self-treatment behavior may be modest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eB. Objective adherence validation (consenting subset)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysicians\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsented to pharmacy linkage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjective subset size (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacy PDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 [0.57\u0026ndash;0.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 [0.53\u0026ndash;0.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePill-count adherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 [0.56\u0026ndash;0.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 [0.55\u0026ndash;0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjective adherence (PDC\u0026thinsp;\u0026ge;\u0026thinsp;0.80), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreement MMAS vs PDC (κ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation MMAS-8 vs PDC (ρ, p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.17 (p\u0026thinsp;=\u0026thinsp;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (p\u0026thinsp;=\u0026thinsp;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eP values are shown where formal statistical comparisons were performed. Agreement (κ) and correlation coefficients are descriptive measures; therefore, no p values are reported for between-group comparisons.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA subset of participants consented to pharmacy linkage and pill-count assessment for objective adherence evaluation. Consent was obtained from 90 physicians (36.0%) and 107 controls (42.8%), with no statistically significant difference in consent rates (p\u0026thinsp;=\u0026thinsp;0.143).\u003c/p\u003e \u003cp\u003eObjective adherence measures are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. Median pharmacy refill adherence (PDC) was identical in both groups (0.71), with overlapping interquartile ranges (physicians: 0.71 [0.57\u0026ndash;0.92] vs controls: 0.71 [0.53\u0026ndash;0.86]; p\u0026thinsp;=\u0026thinsp;0.403). Pill-count adherence was similarly comparable between physicians and controls (0.68 [0.56\u0026ndash;0.78] vs 0.71 [0.55\u0026ndash;0.85]; p\u0026thinsp;=\u0026thinsp;0.459). Adequate objective adherence (PDC\u0026thinsp;\u0026ge;\u0026thinsp;0.80) was observed in 37.8% of physicians and 38.3% of controls (p\u0026thinsp;=\u0026thinsp;1.000), indicating no measurable difference between groups in refill-based persistence.\u003c/p\u003e \u003cp\u003eAgreement between self-reported adherence (MMAS-8) and objective adherence (PDC) was poor in both groups (κ = \u0026minus;0.02 in physicians and κ\u0026thinsp;=\u0026thinsp;0.07 in controls). Similarly, correlations between MMAS-8 scores and PDC were weak and non-significant (ρ = \u0026minus;0.17, p\u0026thinsp;=\u0026thinsp;0.12 among physicians; ρ\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;=\u0026thinsp;0.41 among controls). These findings suggest substantial discordance between subjective and objective adherence measures across both physicians and controls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMultivariable logistic regression predicting adequate adherence (all participants)\u003c/b\u003e \u003cem\u003eOutcome: adequate adherence (yes/no). Complete-case analysis.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysician vs control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.77\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.58\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic conditions (per +\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.81\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.94\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications (per +\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.26 (1.03\u0026ndash;1.54)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolypharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.30\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ-9 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD-7 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-treatment score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.03 (1.01\u0026ndash;1.05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.63\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis was performed to identify factors independently associated with adequate adherence (MMAS-8-based) across the full study population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjustment for demographic, clinical, and psychological variables, physician status was not significantly associated with adequate adherence (adjusted OR 1.15, 95% CI 0.77\u0026ndash;1.70; p\u0026thinsp;=\u0026thinsp;0.493).\u003c/p\u003e \u003cp\u003eAmong the covariates examined, a higher number of prescribed medications was associated with increased odds of adequate adherence (adjusted OR 1.26 per additional medication, 95% CI 1.03\u0026ndash;1.54; p\u0026thinsp;=\u0026thinsp;0.023). In addition, higher self-treatment scores were independently associated with slightly increased odds of adequate adherence (adjusted OR 1.03 per unit increase, 95% CI 1.01\u0026ndash;1.05; p\u0026thinsp;=\u0026thinsp;0.014). Polypharmacy was not significantly associated with adherence (adjusted OR 0.59, 95% CI 0.30\u0026ndash;1.17; p\u0026thinsp;=\u0026thinsp;0.132).\u003c/p\u003e \u003cp\u003eNo statistically significant associations were observed for age, sex, number of chronic conditions, disease duration, depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), burnout score, or insurance coverage (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). Overall, the multivariable analysis suggests that adherence behavior was not determined by professional status but was more closely associated with medication-related burden and self-treatment behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysician-only predictors of adequate adherence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.41\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedications (per +\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.38 (1.01\u0026ndash;1.88)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-treatment score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.04 (1.00\u0026ndash;1.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly work hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNight shifts/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.86\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialty (surgical vs medical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.44\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA separate multivariable model was constructed among physicians only to explore occupational and behavioral predictors of adherence (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this model, a higher number of prescribed medications was significantly associated with adequate adherence (adjusted OR 1.38 per additional medication; 95% CI, 1.01\u0026ndash;1.88; p\u0026thinsp;=\u0026thinsp;0.041). The self-treatment score was also significantly associated with adherence (adjusted OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI\u0026thinsp;=\u0026thinsp;1.00\u0026ndash;1.08; p\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e \u003cp\u003eOccupational characteristics, including weekly working hours (p\u0026thinsp;=\u0026thinsp;0.728), number of night shifts per month (p\u0026thinsp;=\u0026thinsp;0.827), and specialty type (surgical vs medical; p\u0026thinsp;=\u0026thinsp;0.740), were not significantly associated with adherence. Burnout was also not significantly associated with adherence within the physician subgroup (p\u0026thinsp;=\u0026thinsp;0.648).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study found no significant difference in medication adherence between physicians with chronic illness and matched non-physician controls, based on both self-reported and objective adherence measures. This lack of a large group effect implies that medical knowledge alone is not a guarantee of optimal adherence behavior. Instead, adherence appears to reflect a broader behavioral and psychological process that is both motivated and constrained by self-management styles and situational impediments, to which even healthcare workers are not exempt. High medical literacy may not eliminate specific psychological barriers to adherence among physicians. Clinicians may minimize chronic illness or put off suitable self-care due to symptom normalization, fear of being perceived as vulnerable, and worries about professional stigma. Furthermore, role conflict as both a caregiver and a patient can undermine consistent adherence to treatment routines, a common cause of adherence issues observed among other non-physician groups.\u003c/p\u003e \u003cp\u003eThere was no significant difference between the groups in medication adherence, as measured by three self-report measures (MMAS-8, ARMS, GMAS) and by objective measures (pharmacy PDC and pill count), in the consenting group in this cross-sectional comparison of physicians with chronic illness versus matched non-physician controls. This difference in the null group is significant because the physicians were more educated and earned more, yet behavioral adherence was comparable. The observation supports modern adherence models, which conceptualize medication taking as a behavioral process influenced by multidimensional determinants such as opportunity, capability, and motivation, rather than by health knowledge alone [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Self-treatment behavior in this context may not necessarily reflect neglect or irresponsibility, but instead a form of perceived control and patient agency. Participants who engage in self-adjustment or self-management may feel more autonomy over their treatment, which can paradoxically support persistence and routine medication taking. However, such behaviors may also carry clinical risks if undertaken without structured medical supervision, highlighting the importance of guided self-management. From a behavioral perspective, these findings can be interpreted using established psychological frameworks such as the Health Belief Model and self-regulation theory. Even when perceived knowledge is high, adherence depends on perceived susceptibility, perceived necessity of treatment, and the ability to translate intentions into sustained daily behavior. This behavioral\u0026ndash;cognitive gap may explain why physicians do not consistently demonstrate better adherence than non-physician patients.\u003c/p\u003e \u003cp\u003eGiven that physicians may be expected to be more non-adherent due to greater medical knowledge and easier access to health services, our data indicate that professional identity and training do not necessarily lead to improved daily practice. One possible explanation is that physicians face specific obstacles that offset the benefits of their work (time constraints, conflicting interests, symptom normalization, non-continuous self-care). Health services research evidence from the pandemic era demonstrates significant disruptions to usual healthcare use and engagement in chronic care among the general population, which may affect physicians and non-physicians and could compress adherence disparities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, delays in seeking healthcare have been reported among healthcare workers during illness, indicating that professionals do not consistently adhere to optimal self-care practices [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the most important contributions of this study is the finding of poor concordance between self-reported and objective adherence. In our objective subset, there was almost no agreement between MMAS-8 and PDC (k\u0026thinsp;\u0026ge;\u0026thinsp;0), and the correlations were weak and not statistically significant. This is consistent with recent literature on adherence measurement, which suggests that self-report measures, although convenient, may be limited by recall bias and social desirability and may not accurately reflect the implementation and persistence of interventions. A systematic review of patient-reported outcome measures of medication non-adherence highlights wide variability in the types of instruments used to measure intentional or unintentional non-adherence and heterogeneity in psychometric properties across tools [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, a systematic review of self-reported primary care tools emphasizes feasibility but highlights measurement limitations and underscores the importance of careful selection and triangulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Empirical research comparing subjective and refill-based adherence is also reflected in our findings and typically indicates only moderate agreement, even when associations are present [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethodologically, objective measures are not gold standards, but they enhance inference by measuring behavior beyond perception. PDC is popular due to its scalability and its reliance on dispensing data, which is influenced by choices related to the analysis (e.g., definitions of the nominator and assumptions about refill time). The methodological study conducted in 2021 illustrates how various PDC algorithms and assumptions can significantly alter adherence estimates, underscoring the importance of explicit operational definitions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Simultaneously, studies on the optimal cut-offs for applying PDC/MPR in type 2 diabetes indicate that thresholds (typically 0.80) have clinical significance but are not necessarily optimal in other settings and outcomes, which can inform our PDC\u0026thinsp;\u0026ge;\u0026thinsp;0.80 classification [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Collectively, the literature on measurement supports our decision to report continuous adherence distributions (median PDC) and categorical cutoffs, and it places our decision to report discordance with self-report in context.\u003c/p\u003e \u003cp\u003eAmong participants who agreed to pharmacy linkage, there were no significant differences in median PDC between physicians and controls, Pill-count adherence, or the percentage who achieved the PDC\u0026thinsp;\u0026gt;\u0026thinsp;0.80 threshold. This supports the conclusion that the lack of between-group differences is not a self-report artifact. The observation that inconsistencies between self-report and indirect measures are not unusual is supported by evidence from other disease areas [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Accordingly, in our context, the objective subset serves as an internal validation check. Among more adherent physicians, we would expect directional separation in PDC or pill count, but this was not the case.\u003c/p\u003e \u003cp\u003eControl conditions had higher self-treatment scores than physicians, suggesting that controls were more engaged in self-directed medication behaviors than physicians. Modern data have indicated that self-medication is common in adults with chronic illnesses and is commonly driven by barriers to access and convenience, as well as a negative evaluation of symptoms, as opposed to an easy ignoring of medical guidance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. There was a positive association between higher self-treatment scores and greater odds of achieving adequate adherence in our multivariate models (MMAS-8-based). One implication is that self-treatment behavior can be associated with active self-management and high engagement (e.g., purposely planning regimens, symptom-based adjustments) that co-exist with routine adherence- although it does not mean that it is appropriate or safe. The broader evidence on self-management interventions indicates that patient agency or systematic self-management assistance can lead to better outcomes and self-efficacy, yet only with heavy reliance on guidance, follow-up, and system supports [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdequate adherence was independently associated with a greater number of medications in both the overall and physician-only models. Although polypharmacy is commonly associated with regimen complexity and the risk of nonadherence, recent studies indicate that it is heterogeneous and context-dependent, shaped by support structures and the suitability of medications for a patient. A comprehensive, synthesized, up-to-date review of interventions to address polypharmacy indicates that drug reviews and deprescribing interventions may enhance process (appropriateness) but not clinical outcomes; this highlights that polypharmacy is a disease burden as well as a care intensity, not merely too many pills. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Polypharmacy stewardship concept further states that the management of multimorbidity needs to be considered by coordinated actions that should take into account drug-drug interactions, omissions, and individual objectives, once more, stating that the number of medications is not the entire story [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the individual level, patients with higher medication burden may exhibit greater adherence because the illness becomes more salient, patients receive more frequent follow-ups, or routines are more organized. The latest research on polypharmacy has identified drug- and patient-related correlates of adherence, including control intensity and regimen management capability [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The given interpretation is consistent with our baseline comparability: the number of medications and polypharmacy showed similar medians and prevalence in both groups, and the relationship between medications and adherence is more a consequence of behavior and engagement in the shared burden than a disease-severity confounding factor.\u003c/p\u003e \u003cp\u003eWe were unable to identify independent relationships between PHQ-9, GAD-7, burnout, or (in the case of physicians) workload measures and adherence. This null result must be interpreted with caution: mental health can alter adherence through pathways such as motivation, executive function, and a perceived necessity to take medication; however, when multiple determinants are concurrent, and adherence is self-reported, effects may be diluted. Measurement studies of self-report measures indicate that they may assess different dimensions of adherence than objective measures and may fail to capture certain psychological drivers in certain situations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, recent system-level changes in healthcare use and service delivery, such as reduced digital care, may have led individuals to continue taking medications in ways that generic workload metrics have not fully predicted [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe found that a population-agnostic method of assessing adherence and intervention is more effective: clinicians cannot assume that physicians are always adherent, and researchers cannot afford to use self-report measures when high accuracy is needed. There is growing literature on measurement suggesting multimethod assessment (self-report plus pharmacy/refill or other objective indicators), as different instruments identify different stages and mechanisms of adherence. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Treatments must also be multi-dimensional. Evidence syntheses indicate that adherence-support interventions that are implemented in the context of complex chronic illnesses, in most cases, involve the use of combinations (education, follow-up, reminders, structured self-management support, and care coordination) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Digital tools can be beneficial, as they facilitate routines, access, and engagement; however, their effectiveness depends on their implementation and patients' circumstances [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eClinicians should not assume that physicians are inherently adherent to long-term therapy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhysicians living with chronic illness may require the same adherence screening and follow-up support as other patient groups.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEducational knowledge alone is insufficient; behavioral and psychological interventions may be needed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStructured self-management support and stigma-free access to care should be promoted for healthcare professionals.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the findings of this study.\u003c/p\u003e \u003cp\u003eFirst, the cross-sectional design precludes any inference regarding causality or temporal directionality. Associations observed between self-treatment behavior, medication burden, and adherence cannot establish whether these factors precede, follow, or co-develop with adherence patterns. Reverse causation remains plausible; for example, individuals who are generally more engaged in their care may both adhere more consistently and report greater self-directed medication practices.\u003c/p\u003e \u003cp\u003eSecond, adherence was primarily assessed using self-report instruments. Although validated tools were employed, self-reported adherence is susceptible to recall bias and social desirability bias. This concern may be particularly relevant among physicians, whose professional identity could influence reporting patterns. Although objective adherence measures (PDC and pill-count data) were included in a subset of participants, these methods also serve as indirect proxies of medication-taking behavior and do not confirm actual ingestion.\u003c/p\u003e \u003cp\u003eThird, objective adherence analysis was limited to participants who consented to pharmacy linkage, representing approximately one-third of physicians and slightly over two-fifths of controls. This introduces the possibility of selection bias, as individuals willing to provide pharmacy data may differ systematically from non-consenters in unmeasured ways (e.g., trust in healthcare systems, privacy concerns, or adherence behavior itself). Therefore, objective adherence findings should be interpreted as internally informative but not necessarily generalizable to the entire cohort.\u003c/p\u003e \u003cp\u003eFourth, the SAS-SMP instrument measures a broad spectrum of self-directed medication behaviors without differentiating between structured self-management, informal dose adjustments, and potentially unsafe self-medication practices. As such, higher scores may reflect patient autonomy and engagement, access-related adaptations, or clinically suboptimal behaviors. The instrument does not distinguish between beneficial and potentially harmful self-treatment practices, thereby limiting interpretive precision. Future research using more granular assessments of self-management behaviors would help clarify these distinctions.\u003c/p\u003e \u003cp\u003eFifth, although physicians and controls were matched by age, sex, and disease category, substantial differences in socioeconomic indicators (education and income) remained. At the same time, these variables were considered in multivariable analyses where applicable; residual confounding cannot be excluded. Additional unmeasured factors\u0026mdash;such as health literacy, self-efficacy, illness perceptions, healthcare continuity, or medication class\u0026ndash;specific characteristics\u0026mdash;may have influenced adherence behavior.\u003c/p\u003e \u003cp\u003eSixth, disease severity and clinical control were assessed using available clinical indicators; however, these may not fully capture the complexity of multimorbidity or treatment burden. Detailed medication-class\u0026ndash;specific adherence patterns were not analyzed, and heterogeneity across chronic conditions may have attenuated condition-specific effects.\u003c/p\u003e \u003cp\u003eFinally, the study was conducted within a single institutional setting in Upper Egypt, which may limit generalizability to other healthcare systems, cultural contexts, or physician populations with different practice environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this cross-sectional comparative study, medication adherence among physicians with chronic illness was broadly similar to that of matched non-physician controls, as assessed by both self-reported and objective measures. After adjustment for demographic, clinical, and psychological variables, professional status was not independently associated with adequate adherence.\u003c/p\u003e \u003cp\u003eAdherence behavior appeared more closely associated with treatment-related and behavioral factors\u0026mdash;particularly medication burden and self-reported self-treatment practices\u0026mdash;than with occupational characteristics or professional background. However, the observed association between self-treatment behavior and adherence should be interpreted cautiously, as the self-treatment measure encompasses heterogeneous practices that may reflect patient autonomy, access-related adaptations, or potentially unsafe medication behaviors.\u003c/p\u003e \u003cp\u003eThe limited concordance between self-reported and objective adherence measures further underscores the complexity of adherence assessment and highlights the importance of multimodal measurement approaches in future research.\u003c/p\u003e \u003cp\u003eGiven the cross-sectional design and potential residual confounding, these findings should be regarded as hypothesis-generating. Longitudinal studies employing finer-grained assessments of self-management behavior and condition-specific adherence patterns are needed to clarify the mechanisms underlying medication-taking behavior among physicians and other patient populations with chronic illness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMAS-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e8-item Morisky Medication Adherence Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdherence to Refills and Medications Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGMAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Medication Adherence Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAS-SMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Administration Scale for Self-Medication Practices\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Health Questionnaire-9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAD-7\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Anxiety Disorder-7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProportion of Days Covered\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was carried out in accordance with the ethical principles of the Declaration of Helsinki. Luxmed-070126-108 was an IRB local approval accepted and applied by the Research Ethics Committee of the Faculty of Medicine, Luxor University, Luxor, Egypt (date of approval: 07 January 2026). Informed consent was obtained in writing, and all participants provided written consent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. In this manuscript, no personal data of a person in any form, including identifiable information, photographs, or video records, is given.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets produced and/or measured in the present study cannot be made publicly available for ethical and privacy reasons; nevertheless, they can be retrieved by the respective author upon reasonable request and in accordance with the Faculty of Medicine, Luxor University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not funded by any specific grant, whether in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing financial or non-financial interests related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.J.N.A. contributed to study conception and design, data collection, and manuscript drafting.\u003cbr\u003e\u0026nbsp;M.A.B. contributed to data analysis, interpretation of results, and critical revision of the manuscript.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants for their time and cooperation. The authors also acknowledge the Faculty of Medicine, Luxor University, for facilitating the ethical approval and conduct of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAhmed Jado Nabih Ali and Mahmoud Ahmed Bekiet are lecturers at the Faculty of Medicine, Luxor University, Luxor, Egypt.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBurnier M. The role of adherence in patients with chronic diseases. Eur J Intern Med. 2024;119:1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnorrerova P, Matalova P, Wawruch M. Medication Adherence and Intervention Strategies: Why Should We Care. Bratislava Med J 2025 Jun 10:1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunbar-Jacob J, Zhao J. Medication Adherence Measurement in Chronic Diseases: A State-of-the-Art Review of the Literature. Nurs Rep. 2025;15(10):370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira HC, Hayashi D, Carvalho SD, Barros RD, Neves ML, Andrechuk CR, Alexandre NM, Ribeiro PA, Rodrigues RC. Quality of measurement properties of medication adherence instruments in cardiovascular diseases and type 2 diabetes mellitus: a systematic review and meta-analysis. Syst reviews. 2023;12(1):222.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanafelt TD, West CP, Dyrbye LN, Trockel M, Tutty M, Wang H, Carlasare LE, Sinsky C. Changes in burnout and satisfaction with work-life integration in physicians during the first 2 years of the COVID-19 pandemic. InMayo Clinic Proceedings 2022 Dec 1 (Vol. 97, No. 12, pp. 2248\u0026ndash;2258). Elsevier.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaracaldo-Santamar\u0026iacute;a D, Trujillo-Moreno MJ, P\u0026eacute;rez-Acosta AM, Feliciano-Alfonso JE, Calderon-Ospina CA, Soler F. Definition of self-medication: a scoping review. Therapeutic Adv drug Saf. 2022;13:20420986221127501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Liu J, Tang PK, Hu H, Ung CO. A systematic review of self-medication practice during the COVID-19 pandemic: implications for pharmacy practice in supporting public health measures. Front Public Health. 2023;11:1184882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Hu J, Zheng Y, Dai T. Factors affecting medication adherence in patients with chronic diseases: A systematic literature review. Chin Gen Pract J. 2025;2(3):100072.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFahrni ML, Saman KM, Alkhoshaiban AS, Naimat F, Ramzan F, Isa KA. Patient-reported outcome measures to detect intentional, mixed, or unintentional non-adherence to medication: a systematic review. BMJ Open. 2022;12(9):e057868.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoynihan R, Sanders S, Michaleff ZA, Scott AM, Clark J, To EJ, Jones M, Kitchener E, Fox M, Johansson M, Lang E. Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review. BMJ Open. 2021;11(3):e045343.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGertz AH, Pollack CC, Schultheiss MD, Brownstein JS. Delayed medical care and underlying health in the United States during the COVID-19 pandemic: a cross-sectional study. Prev Med Rep. 2022;28:101882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Wilton A, Kilich E, Chaudhry Z, Bell LC, Gahir J, Cadman J, Lever RA, Logan SA. Delayed healthcare seeking and prolonged illness in healthcare workers during the COVID-19 pandemic: a single-centre observational study. BMJ open. 2020;10(11):e040216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRickles NM, Mulrooney M, Sobieraj D, Hernandez AV, Manzey LL, Gouveia-Pisano JA, Townsend KA, Luder H, Cappelleri JC, Possidente CJ. A systematic review of primary care-focused, self-reported medication adherence tools. J Am Pharmacists Association. 2023;63(2):477\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurali KM, Mullan J, Roodenrys S, Cheikh Hassan HI, Lonergan MA. Exploring the agreement between self-reported medication adherence and pharmacy refill-based measures in patients with kidney disease. Patient preference adherence 2022 Dec 31:3465\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrieto-Merino D, Mulick A, Armstrong C, Hoult H, Fawcett S, Eliasson L, Clifford S. Estimating proportion of days covered (PDC) using real-world online medicine suppliers\u0026rsquo; datasets. J Pharm Policy Pract. 2021;14(1):113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim MT, Ab Rahman N, Teh XR, Chan CL, Thevendran S, Ahmad Hamdi N, Lim KK, Sivasampu S. Optimal cut-off points for adherence measure among patients with type 2 diabetes in primary care clinics: a retrospective analysis. Therapeutic Adv Chronic Disease. 2021;12:2040622321990264.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao R, Tang Z, Zhang N, Hu L, Chang Z, Ren J, Bai X, Shi J, Fan S, Pei R, Du L. Discrepancies between self-reported medication in adherence and indirect measurement adherence among patients undergoing antiretroviral therapy: a systematic review. Infect Dis Poverty. 2024;13(04):1\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlwhaibi M, Malik SB, Alswailem L, Alruthia Y. Self-medication among adults with chronic health conditions: a population-based cross-sectional survey in Saudi Arabia. BMJ open. 2023;13(4):e069206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi\u0026ntilde;o de Guzm\u0026aacute;n Quispe E, Mart\u0026iacute;nez Garc\u0026iacute;a L, Orrego Villagr\u0026aacute;n C, Heijmans M, Sunol R, Fraile-Navarro D, P\u0026eacute;rez-Bracchiglione J, Ninov L, Salas-Gama K, Viteri Garc\u0026iacute;a A, Alonso-Coello P. The perspectives of patients with chronic diseases and their caregivers on self-management interventions: a scoping review of reviews. Patient-Patient-Centered Outcomes Res. 2021;14(6):719\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller MS, Qureshi N, Mays AM, Sarkisian CA, Pevnick JM. Cumulative update of a systematic overview evaluating interventions addressing polypharmacy. JAMA Netw Open. 2024;7(1):e2350963.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Yu Y, Yan S, Zeng Y, Su S, He T, Wang Z, Ding Q, Zhang R, Li W, Wang X. Risk factors for self-reported medication adherence in community-dwelling older patients with multimorbidity and polypharmacy: a multicenter cross-sectional study. BMC Geriatr. 2023;23(1):75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLammila-Escalera E, Greenfield G, Pan Z, Nicholls D, Majeed A, Hayhoe BW. A systematic review of interventions to improve medication adherence in adults with mental-physical multimorbidity in primary care. Br J Gen Pract. 2024 Mar 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerardinelli D, Conti A, Hasnaoui A, Casabona E, Martin B, Campagna S, Dimonte V. Nurse-led interventions for improving medication adherence in chronic diseases: A systematic review. InHealthcare 2024 Nov 22 (Vol. 12, No. 23, p. 2337). MDPI.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrands MR, Gouw SC, Beestrum M, Cronin RM, Fijnvandraat K, Badawy SM. Patient-centered digital health records and their effects on health outcomes: systematic review. J Med Internet Res. 2022;24(12):e43086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuach S, Michaelchuk W, Benoit A, Oliveira A, Packham TL, Goldstein R, Brooks D. Mobile health applications for self-management in chronic lung disease: a systematic review. Netw Model Anal Health Inf Bioinf. 2023;12(1):25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"medication adherence, physicians, chronic illness, self-treatment, mental health, pharmacy refill, health behavior","lastPublishedDoi":"10.21203/rs.3.rs-8888407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8888407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMedication adherence in chronic illness is influenced by behavioral, psychological, and contextual factors. Whether physicians\u0026mdash;despite high medical literacy\u0026mdash;demonstrate superior adherence compared with non-physician patients remains unclear. This study compared medication adherence between physicians with chronic illness and matched non-physician controls and examined psychological and behavioral correlates, including self-treatment practices.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional comparative study was conducted among 250 physicians and 250 age-, sex-, and disease-category\u0026ndash;matched non-physician adults receiving long-term pharmacotherapy at university outpatient clinics in Upper Egypt. Self-reported adherence was assessed using the 8-item Morisky Medication Adherence Scale (MMAS-8) as the primary adherence measure. Self-treatment behavior, depressive symptoms, anxiety, and burnout were evaluated using validated instruments. In a consenting subset, objective adherence was estimated using pharmacy refill data (proportion of days covered [PDC]) and documented pill-count records. Multivariable logistic regression models were used to examine factors associated with adequate adherence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of adequate self-reported adherence did not differ significantly between physicians and controls (28.4% vs. 31.2%, p\u0026thinsp;=\u0026thinsp;0.557). Among participants who consented to pharmacy linkage (36% physicians; 43% controls), median PDC and the proportion achieving PDC\u0026thinsp;\u0026ge;\u0026thinsp;0.80 were comparable between groups. Agreement between self-reported and objective adherence was poor in both cohorts. In adjusted analyses, physician status was not independently associated with adequate adherence. A higher number of prescribed medications and greater engagement in self-treatment behaviors were independently associated with higher odds of adequate self-reported adherence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePhysicians with chronic illness demonstrated adherence patterns comparable to matched non-physician patients. Professional training and medical knowledge were not independently associated with better adherence. Behavioral engagement and treatment-related factors appeared more strongly associated with adherence than occupational status. The limited concordance between self-reported and objective adherence highlights the importance of multimethod assessment in adherence research.\u003c/p\u003e","manuscriptTitle":"When Medical Knowledge Is Not Enough: Medication Adherence and Self- Treatment Among Physicians With Chronic Illness in Upper Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 11:44:18","doi":"10.21203/rs.3.rs-8888407/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T11:35:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T14:55:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235861915499540213396358683924613228271","date":"2026-03-31T14:52:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T02:53:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275892154025044622164367260088600407930","date":"2026-03-26T06:37:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T09:32:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-28T19:50:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T05:15:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T05:14:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Mental Health","date":"2026-02-15T20:57:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6a4d30e-5853-42e2-9ab8-6f0be842979a","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T14:08:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 11:44:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8888407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8888407","identity":"rs-8888407","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}