https://orcid.org/0000-0002-8258-8746
Figures
Abstract
Background
Depression is highly prevalent among medical trainees and negatively affects academic performance, empathy, and patient care. While healthy lifestyle behaviors are known to support mental health, the contribution of cognitive-emotional orientations, such as Positive Mental Attitude (PMA), remains underexplored. This study examined the associations between health behaviors, PMA, and depressive symptoms among medical students and residents in Ecuador.
Methods
A cross-sectional survey was conducted in 2025 among 700 medical students and postgraduate residents at a private university. Participants completed the Patient Health Questionnaire-9 (PHQ-9) and the Health Behavior Inventory (HBI). Logistic regression models estimated associations between total HBI scores, its four subscales, and screening positive for clinically significant depressive symptoms (PHQ-9 ≥ 10), adjusting for sociodemographic and academic variables.
Results
Nearly half of participants screened positive on the PHQ-9 (score ≥10) (47.7%). Higher total HBI scores were associated with lower odds of depressive symptoms (AOR = 0.94, 95% CI: 0.93–0.95). When HBI subscales were analyzed simultaneously, only PMA remained independently associated with lower odds of depressive symptoms (AOR = 0.79, 95% CI: 0.74–0.84). Female gender, younger age, and the presence of comorbidities were also associated with higher odds of depressive symptoms.
Conclusions
PMA emerged as the behavioral domain most strongly associated with lower odds of clinically significant depressive symptoms in medical trainees, after adjustment for other lifestyle domains. These findings underscore the relevance of cognitive-emotional orientations in behavioral medicine and support the inclusion of positive psychology approaches in medical education to promote well-being.
Citation: Sánchez X, Carrera P, Cortez MD, Cruz A, Merchán I (2026) Positive mental attitude and depressive symptoms among medical students and residents: a cross-sectional study. PLoS One 21(7): e0354032. https://doi.org/10.1371/journal.pone.0354032
Editor: Vincenzo De Luca, University of Toronto, CANADA
Received: December 31, 2025; Accepted: July 2, 2026; Published: July 17, 2026
Copyright: © 2026 Sánchez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data for this study are publicly available from the Mendeley Data repository (https://data.mendeley.com/preview/vx7fdzkjj5).
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Depression is one of the leading causes of disability worldwide and a major contributor to the global burden of disease [1]. Medical students and residents are particularly vulnerable, with prevalence rates of depressive symptoms consistently exceeding those of the general population [2,3]. This elevated risk has been linked to academic overload, long working hours, emotional demands of clinical care, and lack of adequate support structures [4, 5]. Beyond individual well-being, depressive symptoms negatively affect academic performance, empathy, and patient care [4,5], underscoring its relevance for public health and medical education.
While most research has focused on identifying risk factors for psychopathology, recent conceptual advances highlight the need to also investigate protective factors that sustain mental health. The World Health Organization conceptualizes mental health not merely as the absence of illness, but as a state of well-being encompassing functional capacity, resilience, and social contribution [6]. The dual-factor model distinguishes mental illness from positive mental health [7]. Evidence suggests that positive mental health (PMH)—encompassing optimism, self-acceptance, purpose in life, and positive relationships—independently predicts adaptive functioning, resilience, and remission from psychiatric disorders [8–10].
Within the framework of behavioral medicine, lifestyle practices such as physical activity, balanced diet, preventive care, and stress management are consistently associated with improved psychological outcomes [11]. Emerging evidence suggests that cognitive–emotional orientations represent an additional and distinct pathway through which health behaviors may be linked to mental and physical health outcomes. Optimism, for example, predicts lower incidence of coronary heart disease and reduced all-cause mortality [12–14], while positive affect has been linked to lower levels of inflammatory markers and stress-related physiological responses [15]. Within this perspective, Positive Mental Attitude (PMA) is especially relevant. Closely aligned with constructs such as optimism, resilience, and cognitive reappraisal, PMA reflects a tendency to sustain constructive emotional orientations in the face of adversity [16]. A growing body of evidence indicates that positive attitudes mitigate psychological distress and promote adaptive coping and long-term health benefits, as highlighted in recent integrative reviews [17]. However, little is known about how such orientations operate in medical trainees, a group uniquely exposed to chronic stressors and at elevated risk of mental health problems.
To address this gap, we examined the relationship between health behaviors and depressive symptoms in a large sample of medical students and postgraduate residents from a Latin American institution. Given the sustained exposure of medical trainees to academic, emotional, and clinical stressors, cognitive–emotional orientations such as PMA may play a particularly important role in shaping psychological responses to stress. Unlike other lifestyle domains, PMA reflects internal coping and appraisal processes, which may directly influence vulnerability to depressive symptoms in high-demand training environments. Using the Health Behavior Inventory (HBI), we investigated whether overall lifestyle patterns and specific behavioral domains, including PMA, were associated with depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9). We hypothesized that while general healthy behaviors would be associated with lower odds of depressive symptoms, PMA would emerge as the most influential factor associated with psychological well-being in this population.
Understanding depressive symptoms among medical students and residents is particularly important given their dual role as learners and future healthcare providers. High psychological distress during training has been associated not only with impaired academic performance and professional development, but also with long-term consequences for workforce sustainability and patient care quality [4,18]. Identifying behavioral and cognitive–emotional correlates of depressive symptoms may therefore inform early, scalable strategies to support well-being during medical training.
Methods
Study design and setting
We conducted a cross-sectional study among undergraduate medical students and postgraduate residents at a private university in Ecuador, recognized nationally for medical education and specialty training. Data were collected from February 13 to March 31, 2025, during a standard academic term, in order to capture typical mental health and behavioral patterns while avoiding examination periods.
Participants
The eligible population included all medical students (1,280) and residents (1,122) enrolled in 2025. The target sample size was calculated based on the total eligible population of undergraduate students and postgraduate residents enrolled in 2025. Assuming a 95% confidence level, a 5% margin of error, and a conservative prevalence estimate of 50% (to maximize variance in the absence of prior data), the finite population correction formula was applied, yielding a minimum required sample size of 287 participants.
Although this threshold was established to ensure adequate statistical precision for the primary outcome (clinically significant depressive symptoms, PHQ-9 ≥ 10), recruitment invitations were sent to the entire eligible population (census approach). A total of 700 participants (393 undergraduate students and 307 residents) completed the survey, exceeding the minimum required sample size and thereby increasing the precision of the estimates. Inclusion criteria were current enrollment and provision of informed consent.
Ethical approval was obtained from the PUCE Human Research Ethics Committee (approval number: EO-075–2024). The study was conducted in accordance with the principles of the Declaration of Helsinki. All participants provided informed consent electronically prior to participation. The online survey included an information sheet detailing the study objectives, procedures, risks, and confidentiality, and participants were required to indicate their consent by selecting an agreement option before accessing the questionnaire.
Measures
Depressive symptoms.
Depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9), a validated nine-item scale measuring symptom frequency over the prior two weeks (score range 0–27). A threshold of ≥10 was applied to define screening positive for clinically significant depressive symptoms, consistent with established sensitivity and specificity [19]. The Spanish version used has demonstrated strong psychometric properties in Ecuadorian populations [20,21]. Internal consistency in this sample was excellent (Cronbach’s α = 0.93).
Health behaviors
The Health Behavior Inventory (HBI) [22,23] was used to assess health behaviors across four subscales: Healthy Eating Habits (EH), Preventive Behaviors (PB), Positive Mental Attitude (PMA), and Health Practices (HP). Items are rated on a five-point Likert scale (1 = almost never to 5 = almost always). The total HBI score is calculated as the sum of all 24 items, yielding a range from 24 to 120, with higher scores indicating healthier behaviors. Each of the four subscales consists of 6 items, with subscale scores ranging from 6 to 30. Example items include behaviors related to dietary patterns, preventive practices, and cognitive–emotional regulation (e.g., “I avoid situations that depress me,” “I try to avoid stress and tensions,” and “I think positive”). The Positive Mental Attitude (PMA) subscale captures cognitive and behavioral strategies related to emotional regulation. Although conceptually distinct from the clinical symptoms assessed by the PHQ-9, PMA reflects cognitive–emotional orientations such as optimism and self-regulation, which may be closely related to affective states. As such, some degree of conceptual overlap between PMA and depressive symptomatology cannot be excluded and should be considered when interpreting associations. A Spanish adaptation of the HBI, pilot-tested for linguistic clarity and suitability for Ecuadorian medical trainees, was used. The pilot included 15 residents, who provided feedback on item comprehension and survey flow; no substantive modifications were required. However, formal psychometric validation (e.g., factor structure, convergent validity, test–retest reliability) was not conducted in this study, which should be considered when interpreting the findings. Reliability in this sample was excellent (Cronbach’s α = 0.91).
Covariates
Several sociodemographic and training-related variables were collected to contextualize outcomes. Sociodemographic variables included age (years), gender (male/female), marital status (single/married/divorced/stable union), children (yes/no), religious practice (yes/no), comorbidities (yes/no), living arrangement (alone/with family/with friends), and financial dependents (yes/no). Study hours and night shifts were reported as weekly averages. Night shifts applied primarily to postgraduate residents; undergraduate students typically reported none. Employment referred to paid work outside formal residency training. Alcohol use was assessed as a binary variable based on self-reported current consumption (yes/no). Tobacco use was assessed as a binary variable (“Do you currently use tobacco?” including cigarettes or cigars).
Data collection
A secure online survey was distributed through institutional mailing lists. Unique access links ensured single responses, and weekly reminders were sent across four weeks. Data were collected anonymously and stored on a password-protected server. Only fully completed questionnaires were included in the analysis; partially completed surveys were excluded prior to analysis.
Statistical analysis
Statistical analyses were conducted using R version 4.3.2. Descriptive statistics were used to summarize the characteristics of the sample. Categorical variables were presented as frequencies and percentages, while continuous variables were summarized using medians and interquartile ranges or means and standard deviations, as appropriate according to their distribution. Differences between participants with and without clinically significant depressive symptoms (PHQ-9 ≥ 10) were assessed using chi-square tests or Wilcoxon rank-sum tests, as appropriate.
Univariate logistic regression analyses were performed to examine the association between each independent variable and screening positive for clinically significant depressive symptoms (PHQ-9 ≥ 10). Variables with p ≤ 0.10 in univariate analyses, together with variables considered clinically relevant, were included in multivariable models. Additionally, models adjusted only for age and sex were fitted because these variables are well-established correlates of both depressive symptoms and health behaviors. These intermediate models were used to assess the robustness of the observed associations before inclusion of the full set of covariates.
Two multivariable models were then fitted. Model A was estimated using mixed-effects logistic regression including the total HBI score and selected covariates, with a random intercept for academic program (undergraduate vs postgraduate). This approach was used to account for potential clustering of participants within training programs, which may differ in workload, academic demands, and clinical responsibilities, potentially influencing mental health outcomes.
Model B included the four HBI subscales simultaneously, together with selected covariates, in order to assess their independent associations with depressive symptoms. The same mixed-effects structure was initially specified; however, the model showed singularity, indicating insufficient variability at the random-effect level to support reliable estimation of the mixed-effects structure. As a result, the inclusion of a random intercept was not statistically justified, and the final Model B was therefore estimated using fixed-effects logistic regression to ensure model stability and valid inference.
Model selection was guided by the Akaike Information Criterion (AIC). Odds ratios (ORs) and 95% confidence intervals (CIs) were reported, and statistical significance was set at p < 0.05 using two-tailed tests. Model assumptions were evaluated, including assessment of multicollinearity, model fit, and convergence, and no violations that would compromise the validity of the analyses were identified. Given the hypothesis-driven nature of the analyses and the pre-specification of exposure variables, no adjustment for multiple comparisons was applied.
Results
Sample characteristics
A total of 700 participants were included, comprising 393 undergraduates (56.14%) and 307 postgraduate residents (43.86%). The median age was 24 years (IQR 21–33), and 67.43% were female. Most participants were single (78.57%), lived with family (81.00%), and reported no comorbidities (77.43%).
When stratified by depressive symptoms, participants with clinically significant depressive symptoms (PHQ-9 ≥ 10) were younger, more frequently female, and more likely to be undergraduate students compared to those without depressive symptoms. They also had a higher prevalence of comorbidities and were less likely to report night shifts. In addition, participants with depressive symptoms had significantly lower HBI total scores compared to those without depressive symptoms (median 68.00 vs. 77.00; p < 0.001). Other sociodemographic and behavioral characteristics showed no consistent differences between groups (Table 1).
Among participants reporting comorbidities, mental health conditions were the most frequent (43.67%), followed by endocrine-metabolic (18.98%) and cardiovascular conditions (12.66%).
Prevalence of healthy behaviors and clinically significant depressive symptoms
Table 2 presents the distribution of HBI subscale scores according to depressive symptom status. Participants with PHQ-9 ≥ 10 had significantly lower scores across all subscales compared to those without depressive symptoms (all p < 0.001).
Overall, 334 participants (47.70%, 95% CI: 44.01–51.41) screened positive for depressive symptoms (PHQ-9 ≥ 10). Participants with PHQ-9 ≥ 10 had significantly lower total HBI scores compared to those without depressive symptoms (Median = 68.00 vs. 77.00; IQR = 60–77 vs. 69–84; p < 0.001). When analyzing the four HBI subscales individually, participants with PHQ-9 ≥ 10 also reported lower scores in Healthy Eating Habits (p < 0.001), Preventive Behaviors (p < 0.001), Positive Mental Attitude (p < 0.001), and Health Practices (p < 0.001), based on Wilcoxon rank-sum tests (Fig 1).
Raincloud plots display the distribution of scores for each subscale (Healthy Eating Habits, Preventive Behaviors, Positive Mental Attitude, and Health Practices), including density estimates, individual observations, and boxplots summarizing the median and interquartile range. Corresponding statistical comparisons between groups are reported in Table 2.
Exploratory analysis
All reported associations refer to clinically significant depressive symptoms as defined by a PHQ-9 score ≥10. Univariate analyses were performed as part of the model-building process (Table 3). Higher HBI scores were associated with lower odds of having a PHQ-9 ≥ 10 (OR = 0.95 per point, 95% CI: 0.94–0.96, p < 0.001). Each HBI subscale showed similar inverse associations: EH (OR = 0.90, 95% CI: 0.87–0.93), PB (OR = 0.92, 95% CI: 0.88–0.95), PMA (OR = 0.79, 95% CI: 0.75–0.82), and HP (OR = 0.87, 95% CI: 0.84–0.91), all p < 0.001. Younger age (<24 years; OR = 1.76, 95% CI: 1.31–2.38, p < 0.001), female gender (OR = 1.78, 95% CI: 1.29–2.46, p < 0.001), comorbidities (OR = 1.73, 95% CI: 1.21–2.48, p = 0.003), and undergraduate status (OR = 1.91, 95% CI: 1.41–2.59, p < 0.001) were also significantly associated. Night shifts were associated with reduced odds (OR = 0.66, 95% CI: 0.49–0.89, p = 0.006). Other variables were not significant.
Multivariate analysis
Table 4 shows the results of Model A. Higher HBI scores remained independently associated with lower odds of depressive symptoms (AOR = 0.94, 95% CI: 0.93–0.95, p < 0.001), indicating that each one-point increase in the HBI score was associated with a 6% reduction in the odds of screening positive for depressive symptoms. Female gender (AOR = 1.76, 95% CI: 1.21–2.55, p = 0.003) and the presence of comorbidities (AOR = 1.90, 95% CI: 1.26–2.84, p = 0.002) were also independently associated.
Table 5 depicts the results of Model B. Only positive mental attitude (PMA) was independently associated with depressive symptoms (AOR = 0.79, 95% CI: 0.74–0.84, p < 0.001), indicating that each one-point increase in the PMA score was associated with a 21% reduction in the odds of screening positive for depressive symptoms. In contrast, EH, PB, and HP were not independently associated after adjustment. Younger age (<24 years; AOR = 1.92, 95% CI: 1.05–3.56, p = 0.036), female gender (AOR = 1.74, 95% CI: 1.18–2.58, p = 0.005), and the presence of comorbidities (AOR = 1.67, 95% CI: 1.10–2.55, p = 0.017) were also significantly associated in this model.
Additional models adjusted only for age and sex showed results broadly consistent with those of the fully adjusted models (S1 Table). In particular, the inverse association between total HBI score and depressive symptoms, as well as the association with the positive mental attitude subscale, remained stable across model specifications.
Discussion
In this cross-sectional study of undergraduate and postgraduate medical trainees, we observed a high prevalence of clinically significant depressive symptoms. Higher total HBI scores were consistently associated with lower odds of depressive symptoms. Among the HBI subscales, positive mental attitude demonstrated the strongest and most consistent independent association observed, whereas other subscales were not significant after adjustment. The independent association between PMA and depressive symptoms that remained after adjustment for other HBI domains and covariates may partly reflect conceptual overlap between PMA and affective states, as well as a bidirectional relationship between cognitive–emotional regulation and depressive symptoms. At the same time, it is also plausible that higher levels of PMA may exert a protective effect by promoting adaptive coping strategies, emotional resilience, and more effective responses to stress, thereby reducing vulnerability to depressive symptoms. Beyond health behaviors, female gender and the presence of comorbidities were also independently associated with depressive symptoms.
Another notable finding was the high prevalence of clinically significant depressive symptoms in our sample. Nearly half of medical students and residents had PHQ-9 scores ≥10 (47.7%), a prevalence substantially higher than that reported in international meta-analyses. Global estimates suggest that approximately 27% of medical students and 29% of residents screen positive for clinically significant depressive symptoms [2,5]. More recent reviews indicate that between one-quarter and one-third of trainees are affected worldwide [24], underscoring that our findings exceed global averages. Evidence from Latin America also points to an elevated burden of depressive symptoms among healthcare workers. During the COVID-19 pandemic, pooled prevalence estimates of depression among healthcare workers across 11 countries ranged from 14.7% to 22.0%, reinforcing a challenge that was evident during the crisis and appears to persist in the aftermath [25]. In addition, a study in Peru reported that approximately 30% of medical students experienced moderate depressive symptoms during the pandemic’s second wave [26], while a study in Mexico identified a prevalence of 49% among medical students [27]. Taken together, these findings suggest that the psychological burden in Latin American medical training settings may exceed global averages, possibly due to systemic educational stressors, cultural stigma around mental health, and resource constraints. Given the consequences of depressive symptoms for empathy, learning, and patient care [4,18], the observed rates underscore an urgent need for regionally tailored support measures and interventions.
Beyond overall prevalence, these findings suggest that certain subgroups may be particularly vulnerable. Specifically, younger participants, females, and those reporting comorbidities consistently showed higher odds of depressive symptoms, indicating a potential clustering of risk factors. Although subgroup-specific analyses were beyond the scope of this study, these patterns highlight the importance of targeted screening and support strategies for higher-risk groups within medical training environments.
Our findings suggest that not all health behavior domains contribute equally to the observed association with depressive symptoms. Although healthier lifestyle patterns overall were associated with lower odds of depressive symptoms, only PMA remained independently associated after adjustment for the other HBI domains and covariates. This finding may indicate that cognitive–emotional processes are particularly relevant in shaping psychological well-being among medical trainees, a population exposed to persistent academic and clinical stressors. While evidence from randomized trials and systematic reviews supports the role of lifestyle interventions, including nutrition, physical activity, sleep, and stress management, in promoting mental health [28–33], our results suggest that cognitive–emotional orientations such as PMA may represent an especially salient component within broader behavioral medicine frameworks. Future studies should explore whether interventions aimed at strengthening adaptive coping, emotional regulation, and resilience can improve mental health outcomes in medical trainees.
Interestingly, performing night shifts was associated with lower odds of depressive symptoms in univariate analysis, a finding that contrasts with existing literature generally linking shift work to adverse mental health outcomes [33,34]. This result may reflect residual confounding, selection effects (e.g., more resilient individuals being more likely to engage in night shifts), contextual factors related to training structure, or the normalization and expectation of night shift work within medical training environments, and should therefore be interpreted with caution.
Beyond their theoretical relevance, these findings also have practical implications for medical training environments. Because PMA was the only HBI domain that remained independently associated with depressive symptoms after adjustment, cognitive–emotional regulation may represent a particularly relevant target for intervention in this population. These findings suggest that screening for cognitive–emotional orientations such as PMA may complement traditional lifestyle assessments in medical training settings. Low-cost interventions aimed at fostering adaptive cognitive framing, emotional regulation, and stress management could be integrated into student support services and residency wellness programs without requiring substantial structural changes. This recommendation is supported by prior evidence indicating that mindfulness-based and cognitive-behavioral interventions can improve stress, anxiety, and depressive symptoms in health-professions students and trainees, although the certainty of evidence varies across intervention types [35–38]. Interventions for medical students may prioritize academic workload management and peer support, whereas residency programs may benefit from duty-hour optimization and cognitive resilience training.
We recognize both the strengths and limitations of our study. Among its strengths, it relied on a large and heterogeneous sample that included both medical students and postgraduate residents, which increases the robustness of the results. The use of validated and widely employed instruments with excellent internal consistency also adds credibility to our findings. Moreover, this study contributes novel evidence from a Latin American medical training context and highlights the relative salience of cognitive–emotional health behaviors compared with traditional lifestyle domains when examined simultaneously. Importantly, by evaluating lifestyle behaviors and cognitive-emotional orientations within the same framework, we were able to demonstrate that PMA stands out as a salient behavioral factor of psychological well-being, adding new insights to the behavioral medicine literature.
At the same time, several limitations must be acknowledged. First, the cross-sectional design prevents the establishment of causal relationships and limits the ability to determine the directionality of the association between health behaviors, including PMA, and depressive symptoms. Additionally, the response rate may introduce potential non-response bias, as individuals with more severe depressive symptoms or poorer health behaviors may have been less likely to participate, potentially leading to an underestimation of the true prevalence and strength of associations. Furthermore, although a Spanish adaptation of the HBI was pilot-tested for clarity and showed good internal consistency, formal psychometric validation in this population was not conducted, which may limit the interpretability of the measured constructs. Although the study was conducted at a single institution, postgraduate residents were recruited from diverse health services and cities across the country, which may partially mitigate this limitation. Finally, because prior diagnoses of depression or other mental disorders were not used as exclusion criteria, it is possible that pre-existing conditions contributed to the observed prevalence and associations, potentially inflating estimates of depressive symptoms.
Although female sex was associated with higher odds of depressive symptoms, we did not perform sex-stratified analyses; future studies could explore potential differences in these associations across sex. In addition, some covariates (e.g., night shifts, paid work, and hours of study) may have different relevance depending on training level; however, models were adjusted for academic program and age to partially account for these differences. Furthermore, the predominance of female participants in the sample may have influenced the observed prevalence and associations. Moreover, the analysis did not stratify by training stage or curricular phase, which may further limit the interpretation of differences across academic levels. Finally, we are also aware that reliance on self-reported measures may introduce bias, and that unmeasured confounders cannot be excluded. Even so, the consistency of the observed associations, together with the integrated assessment of lifestyle and cognitive–emotional domains, supports the contribution of this study and points to clear directions for future multicenter and longitudinal research.
Conclusion
This study shows that clinically significant depressive symptoms are highly prevalent among medical students and residents in this setting, underscoring the persistent psychological burden in this population. While healthier lifestyle behaviors overall were associated with lower odds of depressive symptoms, Positive Mental Attitude emerged as the only behavioral domain independently associated with depressive symptoms in multivariable models. These findings should be interpreted as associative rather than causal and may partly reflect both conceptual proximity and a bidirectional relationship between cognitive–emotional regulation and depressive symptoms. Nevertheless, they highlight the potential relevance of cognitive–emotional orientations within behavioral medicine frameworks and support further longitudinal and interventional research to clarify their role in promoting mental well-being among medical trainees.
Supporting information
S1 Table. Age- and sex-adjusted, and fully adjusted logistic regression models for the association between Health Behavior Inventory (HBI) (total score and subscales) and depressive symptoms.
https://doi.org/10.1371/journal.pone.0354032.s001
(PDF)
Acknowledgments
The authors would like to thank the Pontificia Universidad Católica del Ecuador (PUCE) for providing institutional support to conduct this study. Special thanks are extended to the faculty members who facilitated survey administration and to the research assistants who contributed to data collection.
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