Sociodemographic and Health Correlates of Health-Promoting Behaviors among Nursing Students

1. Introduction

Nursing constitutes the largest workforce in the health sector, comprising about 59% of all health professionals worldwide [1]. In 2025, the International Council of Nurses introduced a new definition of nursing, broadening the profession’s role and emphasizing its importance in building sustainable and equitable health systems. The updated definition recognizes nurses as political leaders and change agents, with a key focus on health promotion to improve overall well-being [2].

Non-communicable diseases (NCDs) are the leading cause of global morbidity and mortality, responsible for 74% of all deaths [3]. In Spain, national mortality data for 2024 showed a similar trend, with cancer (26.6%) and circulatory diseases (26.1%) being the leading causes of death [4]. NCDs also impose significant economic and social burdens, stressing healthcare systems and hindering sustainable development [5]. The World Health Organization’s (WHO) 2023–2030 Roadmap highlights modifiable lifestyle factors such as poor diet, tobacco use, alcohol abuse, and inactivity as major contributors to NCDs, which underscores the need for effective health-promoting strategies to empower individuals and communities to make informed health decisions [5].

A widely used framework for understanding health-promoting behaviors is the Health Promotion Model (HPM), which emphasizes the proactive role individuals play in improving their health. It explores the interaction between personal and environmental factors that shape health outcomes [6]. Within the model, a key concept is the health-promoting lifestyle, recognized as a crucial determinant of health outcomes [7].This lifestyle encompasses behaviors that prevent disease and enhance quality of life by promoting well-being, personal satisfaction, and self-actualization [6,8]. These behaviors include responsible health practices, physical activity, balanced nutrition, stress management, spiritual growth, and positive relationships [6,8].

Universities provide a strategic setting for the implementation of health-promoting initiatives, given their formative influence on the values and behaviors of young adults. Beyond their academic role, universities have an ethical responsibility to promote health-oriented behaviors, especially among students in health-related fields [9], such as nursing, who are expected to serve as role models for health promotion in their future careers [10]. Nevertheless, university life presents challenges such as irregular schedules and unfamiliar environments, which can lead to unhealthy behaviors [11]. With their demanding theoretical and clinical components, nursing programs further increase stress and limit self-care opportunities for students [12]. However, engaging in health-promoting behaviors has been shown to reduce stress and improve resilience, academic performance, and overall well-being [13].

Although nursing students receive extensive education about health and disease prevention, numerous studies have revealed that they exhibit suboptimal health behaviors, specifically insufficient physical activity, poor dietary habits, inadequate stress management, and low personal health accountability [10,14,15,16] despite awareness of their importance.

As future healthcare professionals, nursing students are entering a global healthcare workforce where burnout is prevalent, characterized by emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment [17]. In Spain, the General Council of Nursing (CGE) reported that 90% of nurses faced psychological issues such as stress, anxiety, insomnia, and depression due to work-related pressures, with 23% needing sick leave [18]. Promoting a health-enhancing lifestyle during undergraduate nursing education is essential to prevent health problems and reduce the impact of workplace stress on nursing professionals.

Despite the recognized importance of healthy lifestyle habits, many nursing schools and academic institutions lack structured programs or targeted interventions designed to enhance nursing students’ lifestyle behaviors. This gap undermines their ability to embody health-promoting values and diminishes the likelihood of translating such behaviors into future professional practice. As lifestyle habits are established early and become increasingly resistant to change later in life [19], the university period represents a pivotal time for effective interventions.

In consequence, equipping nursing students with robust health-management skills can not only enhance their personal well-being but also empower them to embody the values of competent health educators and advocates, ultimately serving as a vital investment in the professional integrity and future sustainability of the nursing profession.

Investigating health-promoting behaviors in nursing students is crucial. Despite its importance, there has been limited research on the lifestyle habits of nursing students in Spain, particularly regarding their engagement with the Health-Promoting Lifestyle Profile-II (HPLP-II) [19]. Based on the HPM, the HPLP-II, developed by Walker et al. (1987), has been validated in different cultural contexts, demonstrating its reliability and relevance for both clinical and research purposes [9,20,21,22]. To date, no studies have examined the relationships between HPLP-II scores and various sociodemographic, academic, and health-related factors among nursing students in Spain. This study aimed to (1) examine HPLP-II scores across all six dimensions and their intercorrelations, (2) analyze the relationships between a wide range of sociodemographic, academic, and health-related variables and health-promoting behaviors, and (3) identify the strongest predictors of positive health behaviors among nursing students in Spain.

2. Materials and Methods

Study design and setting

A cross-sectional study was conducted from October to November 2023 among nursing students at the Faculty of Medicine and Nursing of AA.

Study sample

All undergraduate nursing students from 1st to 4th year who provided informed consent, were willing to participate and had no lifestyle-affecting conditions (e.g., multiple sclerosis or musculoskeletal disorders) were eligible to participate.

A standard formula for estimating proportions to determine the mean HPLP-II score was used, with a 95% confidence level (Z=1.96), a conservative prevalence estimate (p=0.50) that maximized variance [23], and a ±5% margin of error (d=0.05). This yielded a sample size of 385 students. The final target sample size was adjusted upward to 424 participants to account for an anticipated 10% non-response rate.

Study instruments

Participants completed a questionnaire consisting of two sections. The first section gathered data on sociodemographic, academic, and health-related characteristics. Sociodemographic and academic variables included sex (female or male), age, year of study (1st, 2nd, 3rd, or 4th), maternal and paternal educational levels (primary, secondary, or higher), parental employment status (working: yes or no), family structure (monoparental or biparental), place of residence (urban or rural), type of habitual residence (family home or flat renting or residence), and employment during studies (yes or no).

Health-related characteristics included prior work experience in health-related fields before entering university (yes or no), engagement in university wellness activities (e.g., sports and health education sessions) (yes or no), awareness of health-promoting lifestyles (yes or no), the presence of any chronic disease not limiting lifestyle (yes or no), smoking status (yes or no), and daily sleep duration. Self-reported sleep time was recorded in hours and minutes and dichotomized as ≥7 hours/day or <7 hours/day, in accordance with expert recommendations for adults [24]. Screen-viewing activities were also assessed by asking students how much time they typically spend on various devices—smartphone, TV, game console, tablet, and computer—on a usual weekday and weekend day. Total screen time for weekdays and weekends was calculated by summing all reported usage and dividing by 7 (weekdays) or 2 (weekend days). Responses were then categorized according to the Canadian 24-Hour Movement Guidelines for adults, which recommend no more than 3 hours/day of recreational screen time [25]. Participants were classified as either meeting the guideline (≤3 hours/day) or exceeding it (>3 hours/day). Finally, mean daily sleep duration and average screen-viewing time were computed for analysis.

Participants self-reported their weight and height, from which Body Mass Index (BMI, kg/m²) was calculated. BMI categories (underweight: <18.5 kg/m², normal: 18.5–24.9 kg/m², overweight: 25–29.9 kg/m², obese: ≥30 kg/m²) were assigned following the Centers for Disease Control and Prevention standards [26]. Adherence to the Mediterranean diet was assessed using the 16-item KIDMED questionnaire [27]. Results were categorized as high (≥8 points), medium (4–7 points), or low adherence (≤3 points), based on the total score obtained. Physical activity habits were evaluated using the short form of the International Physical Activity Questionnaire (IPAQ), a validated tool widely used in adult populations [28]. Obtained results were classified as low, moderate, or high physical activity levels, according to the official IPAQ scoring protocol [29].

The second section of the questionnaire included the HPLP-II to assess health-promoting lifestyle behaviors [19]. This 52-item instrument is organized into six subscales: health responsibility (nine items), physical activity (eight items), stress management (eight items), nutrition (nine items), spiritual growth (nine items), and interpersonal relationships (nine items). Participants respond using a 4-point Likert scale: never (1), sometimes (2), often (3), and routinely (4). The overall HPLP-II score is calculated by averaging the responses across all 52 items, resulting in a score ranging from 1 to 4 (equivalent to a total score of 52–208). Subscale scores are obtained similarly by averaging the responses to the items within each subscale. In line with previous studies, the total scores were categorized in this study as follows: poor (52–90), moderate (91–129), good (130–168), and excellent (169–208) [30]. The original HPLP-II demonstrated excellent internal consistency, with an overall Cronbach’s α of 0.94 and subscale reliabilities ranging from 0.79 to 0.87 [19].

Data Collection

Recruitment was conducted at the conclusion of regular theoretical class sessions, during which students were invited to participate. They were explicitly informed that participation was entirely voluntary and that they could decline or withdraw their consent at any point during the study without the need to provide justification. Throughout data collection, investigators were available to assist participants and address any questions or concerns that arose.

Ethical Considerations

The study protocol was reviewed and approved by the University of the Basque Country Ethics Committee for Research Involving Human Subjects (Approval Code: M10/2023/221). Participants received detailed information about the study’s objectives and procedures and the confidentiality of their data. They were also informed that completion of the questionnaire implied their consent to participate.

Data Analysis

Descriptive statistics were used to summarize demographic characteristics and health-promoting behaviors. Categorical variables were presented as frequencies and percentages, while continuous variables were described using means and standard deviations. The normality of continuous variables was assessed using the Shapiro–Wilk test in combination with graphical methods (histogram analysis and Q–Q plots). Mean scores were calculated for the overall HPLP-II scale as well as for each of its subscales (adjusting the mean scores for the number of items). For variables that did not follow a normal distribution, non-parametric statistical tests were applied (Mann–Whitney U test and Kruskal–Wallis H test) to ensure analytical robustness. Spearman’s rank correlation coefficients were used to analyze the intercorrelations between subscales to examine relationships between the various dimensions of health-promoting behaviors.

Hierarchical multivariate logistic regression analyses were performed to investigate the association between covariates and HPLP-II scores. Model I included sociodemographic variables, while Model II incorporated health-related factors in addition to sociodemographic variables. Model performance was evaluated using chi-square statistics, goodness-of-fit indices such as the −2 log-likelihood (−2LL), and Nagelkerke’s R². A likelihood ratio test assessed whether Model II significantly improved upon Model I. Multicollinearity among covariates was assessed using the variance inflation factor, with values ranging from 1.02 to 2.80. Data were entered using pre-established coding schemes, and all analyses were conducted using the Statistical Package for the Social Sciences (SPSS), version 28.0 (SPSS Inc., Chicago, IL, United States [US]). Statistical significance was set at p<0.05.

3. Results

3.1. Sociodemographic, Academic and Health-Related Characteristics of the Participants

A total of 476 participants were included in the final analysis (response rate: 1st year: 87.1%, 2nd year: 90.6%, 3rd year: 76.1%, 4th year: 37%, overall: 72.1%). Among the participants, 81.1% were women, with ages ranging from 17 to 28 years; the most represented age groups were 19–20 years (40.1%) and 21–22 years (38.8%). Regarding family education, a larger proportion of mothers held a university degree (52.8%), while a larger proportion of fathers had completed primary (18.0%) or secondary (45.9%) education only. Additionally, fathers were employed at a slightly higher rate (85.1%) than mothers (80.5%). The majority of the participants came from biparental families (89.3%), resided in urban areas (92.6%), and lived in family-owned homes (92.0%). The sample included participants from all academic years, and 24.4% were employed (Table 1).

All but one student demonstrated awareness of health-promoting lifestyle behaviors (99.8%). A significant proportion lacked prior experience in the health field (76.9%) and participation in university wellness activities (88.9%). Additionally, the minority reported having a chronic disease (13.2%). Despite this, a small proportion smoked (8.0%), averaged fewer than 7 hours of sleep/night (16.6%), had low adherence to the Mediterranean diet (4.2%), and had a low level of physical activity (9.5%). Furthermore, approximately 9.9% were overweight, and 1.5% were obese. However, the majority did not adhere to recommended screen time guidelines, both on weekdays (79.2%) and weekend days (88.4%).

3.2. HPLP-II Scores

The adjusted mean total scores for the HPLP-II subscales were 2.20±0.48 for health responsibility, 2.47±0.62 for physical activity, 2.72±0.48 for nutrition, 2.87±0.47 for spiritual growth, 3.12±0.44 for interpersonal relations, and 2.33±0.44 for stress management (Table 2). The overall lifestyle-adjusted mean HPLP-II score among the participants was 2.62±0.33. Among the subscales, the participants had the highest score for interpersonal relations and the lowest score for health responsibility, followed by stress management.

On further categorization, 0.0% of the participants scored poor (score: 52–90); 33.0%, moderate (score: 91–129); 62.8%, good (score: 130–168); and 4.2%, excellent (score: 169–208) in the HPLP-II.

The Cronbach’s α for the HPLP-II subscales demonstrated good-to-excellent internal consistency. Specifically, the overall instrument achieved an α of 0.896, while the subscales achieved an α ranging from 0.758 (physical activity) to 0.803 (spiritual growth).

In the comparative analysis (Supplementary Table S1. Appendix A1), we examined the differences in the adjusted mean HPLP-II scores in relation to the sociodemographic, academic, and health-related variables. The older participants, particularly those in their 4th year, demonstrated higher HPLP-II scores (p=0.016), as did those with employed parents (mother: p=0.004; father: p=0.034) and those who worked while studying (p=0.013). Sufficient sleep (p=0.024), lower obesity levels (p=0.019), higher adherence to the Mediterranean diet (p<0.001), and increased physical activity (p<0.001) were also associated with better scores. In contrast, excessive screen time was linked to lower scores (p=0.014).

3.3. Intercorrelations Between the HPLP-II Subscales

The intercorrelations between the subscales were significant and ranged widely (Spearman’s r=0.165–0.564; all p<0.001), confirming that they represent related dimensions of health-promoting lifestyle behaviors (Table 3). The overall lifestyle showed both the strongest and weakest correlations—with spiritual growth (r=0.739) and interpersonal relations (r=0.605), respectively. Among the subscales, the weakest correlation was noted between physical activity and interpersonal relations (r=0.165) and the strongest correlation between spiritual growth and interpersonal relations (r=0.564). In addition, stress management was most strongly correlated with spiritual growth (r=0.528). Finally, physical activity showed the highest correlation with nutrition (r=0.488), and health responsibility was most strongly correlated with spiritual growth (r=0.402).

3.4. Factors Influencing the HPLP-II Scores

The HPLP-II scores were dichotomized into two categories: good (combining the original good and excellent categories) and below average (combining moderate and poor), as the number of observations in the excellent and poor categories was significantly smaller. Table 4 presents the results of the multivariate hierarchical logistic regression analyses of the association between a good HPLP-II score and the sociodemographic–academic and health-related variables.

Table 4. Hierarchical logistic regression analysis of the sociodemographic–academic and health-related characteristics predicting the HPLP-II score among the participants (N=476).

Variable
		Model I		Model II
		AOR (95% CI)	p value	AOR (95% CI)	p value
Sociodemographic–academic characteristics
Sex	Female Male	Ref 0.64 (0.37–1.09)	- 0.097	Ref 0.68 (0.37–1.24)	- 0.214
Age, year	>22 21–22 19–20 17–18	Ref 0.82 (0.44–1.51) 0.79 (0.38–1.62) 0.75 (0.23–2.40)	- 0.524 0.514 0.629	Ref 0.48 (0.21–1.09) 0.44 (0.16–1.25) 0.50 (0.11–2.27)	- 0.081 0.124 0.371
Year of study	4th year 3rd year 2nd year 1st year	Ref 0.52 (0.25–1.08) 0.58 (0.26–1.27) 0.53 (0.23–1.23)	- 0.078 0.174 0.139	Ref 0.58 (0.25–1.30) 0.68 (0.27–1.70) 0.70 (0.26–1.88)	- 0.189 0.411 0.477
Maternal educational level	Higher education Secondary education Primary education	Ref 0.63 (0.40–1.01) 0.59 (0.30–1.14)	- 0.056 0.118	Ref 0.67 (0.40–1.26) 0.79 (0.37–1.66)	- 0.133 0.531
Paternal educational level	Higher education Secondary education Primary education	Ref 0.96 (0.60–1.54) 1.26 (0.67–2.35)	- 0.871 0.477	Ref 1.06 (0.62–1.80) 1.58 (0.77–3.24)	- 0.839 0.211
Mother’s employment status	Yes No	Ref 0.70 (0.43–1.17)	- 0.174	Ref 0.71 (0.40–1.27)	- 0.234
Father’s employment status	Yes No	Ref 0.79 (0.43–1.44)	- 0.433	Ref 0.80 (0.40–1.56)	- 0.514
Family structure	Monoparental Biparental	Ref 1.09 (0.55–2.17)	- 0.798	Ref 0.93 (0.42–2.04)	- 0.858
Place of residence	Urban Rural	Ref 0.89 (0.40–1.94)	- 0.762	Ref 0.91 (0.38–2.17)	- 0.834
Habitual residence	Family home Residence or flat renting	Ref 1.07 (0.51–2.25)	- 0.854	Ref 1.14 (0.49–2.65)	- 0.750
Employment status	Yes No	Ref 0.64 (0.39–1.06)	- 0.081	Ref 0.56 (0.32–0.98)	- 0.042
Health-related characteristics
Previous experience in the health field	Yes No			Ref 1.70 (0.83–3.47)	- 0.149
Participation in university wellness activities	Yes No			Ref 0.83 (0.39–1.77)	- 0.634
Chronic disease	Yes No			Ref 0.93 (0.48–1.80)	- 0.835
Smoking status	Yes No			Ref 1.63 (0.73–3.62)	- 0.232
Sleep duration	≥7 hours/day <7 hours/day			Ref 0.64 (0.35–1.18)	- 0.157
BMI	Underweight Healthy weight Overweight Obesity			Ref 1.18 (0.54–2.61) 1.11 (0.39–3.15) 0.13 (0.01–1.52)	- 0.678 0.844 0.105
KIDMED result (Mediterranean diet adherence)	High adherence Medium adherence Low adherence			Ref 0.30 (0.19–0.49) 0.06 (0.02–0.20)	- <0.001 <0.001
IPAQ result (Physical activity level)	High level Moderate level Low level			Ref 0.52 (0.32–0.85) 0.37 (0.17–0.80)	- 0.008 0.012
Screen-viewing duration (weekday) Screen-viewing duration (weekend day)	≤3 hours/day >3 hours/day ≤3 hours/day >3 hours/day			Ref 1.49 (0.80–2.78) Ref 0.87 (0.38–1.96)	- 0.209 - 0.734
Model chi-square –2 log-likelihood Degree of freedom Nagelkerke R2 Likelihood ratio test		20.31 (p=0.258) 570.25 17 0.059		93.86 (p<0.001) 496.69 31 0.254 73.55 (p<0.001)

Abbreviations: AOR = Adjusted Odds Ratio; CI = Confidence Interval.

Table 4. Hierarchical logistic regression analysis of the sociodemographic–academic and health-related characteristics predicting the HPLP-II score among the participants (N=476).

Variable
		Model I		Model II
		AOR (95% CI)	p value	AOR (95% CI)	p value
Sociodemographic–academic characteristics
Sex	Female Male	Ref 0.64 (0.37–1.09)	- 0.097	Ref 0.68 (0.37–1.24)	- 0.214
Age, year	>22 21–22 19–20 17–18	Ref 0.82 (0.44–1.51) 0.79 (0.38–1.62) 0.75 (0.23–2.40)	- 0.524 0.514 0.629	Ref 0.48 (0.21–1.09) 0.44 (0.16–1.25) 0.50 (0.11–2.27)	- 0.081 0.124 0.371
Year of study	4th year 3rd year 2nd year 1st year	Ref 0.52 (0.25–1.08) 0.58 (0.26–1.27) 0.53 (0.23–1.23)	- 0.078 0.174 0.139	Ref 0.58 (0.25–1.30) 0.68 (0.27–1.70) 0.70 (0.26–1.88)	- 0.189 0.411 0.477
Maternal educational level	Higher education Secondary education Primary education	Ref 0.63 (0.40–1.01) 0.59 (0.30–1.14)	- 0.056 0.118	Ref 0.67 (0.40–1.26) 0.79 (0.37–1.66)	- 0.133 0.531
Paternal educational level	Higher education Secondary education Primary education	Ref 0.96 (0.60–1.54) 1.26 (0.67–2.35)	- 0.871 0.477	Ref 1.06 (0.62–1.80) 1.58 (0.77–3.24)	- 0.839 0.211
Mother’s employment status	Yes No	Ref 0.70 (0.43–1.17)	- 0.174	Ref 0.71 (0.40–1.27)	- 0.234
Father’s employment status	Yes No	Ref 0.79 (0.43–1.44)	- 0.433	Ref 0.80 (0.40–1.56)	- 0.514
Family structure	Monoparental Biparental	Ref 1.09 (0.55–2.17)	- 0.798	Ref 0.93 (0.42–2.04)	- 0.858
Place of residence	Urban Rural	Ref 0.89 (0.40–1.94)	- 0.762	Ref 0.91 (0.38–2.17)	- 0.834
Habitual residence	Family home Residence or flat renting	Ref 1.07 (0.51–2.25)	- 0.854	Ref 1.14 (0.49–2.65)	- 0.750
Employment status	Yes No	Ref 0.64 (0.39–1.06)	- 0.081	Ref 0.56 (0.32–0.98)	- 0.042
Health-related characteristics
Previous experience in the health field	Yes No			Ref 1.70 (0.83–3.47)	- 0.149
Participation in university wellness activities	Yes No			Ref 0.83 (0.39–1.77)	- 0.634
Chronic disease	Yes No			Ref 0.93 (0.48–1.80)	- 0.835
Smoking status	Yes No			Ref 1.63 (0.73–3.62)	- 0.232
Sleep duration	≥7 hours/day <7 hours/day			Ref 0.64 (0.35–1.18)	- 0.157
BMI	Underweight Healthy weight Overweight Obesity			Ref 1.18 (0.54–2.61) 1.11 (0.39–3.15) 0.13 (0.01–1.52)	- 0.678 0.844 0.105
KIDMED result (Mediterranean diet adherence)	High adherence Medium adherence Low adherence			Ref 0.30 (0.19–0.49) 0.06 (0.02–0.20)	- <0.001 <0.001
IPAQ result (Physical activity level)	High level Moderate level Low level			Ref 0.52 (0.32–0.85) 0.37 (0.17–0.80)	- 0.008 0.012
Screen-viewing duration (weekday) Screen-viewing duration (weekend day)	≤3 hours/day >3 hours/day ≤3 hours/day >3 hours/day			Ref 1.49 (0.80–2.78) Ref 0.87 (0.38–1.96)	- 0.209 - 0.734
Model chi-square –2 log-likelihood Degree of freedom Nagelkerke R2 Likelihood ratio test		20.31 (p=0.258) 570.25 17 0.059		93.86 (p<0.001) 496.69 31 0.254 73.55 (p<0.001)

Abbreviations: AOR = Adjusted Odds Ratio; CI = Confidence Interval.

The inclusion of the health-related variables significantly improved the model fit (Model II) over the demographic-only model (Model I). Specifically, the −2LL was reduced from 570.25 to 496.69, indicating a significantly better fit (χ²=93.86, p<0.001). Moreover, Model II explained approximately 25.4% of the variance in the HPLP-II scores (Nagelkerke R²=0.254), demonstrating that adding the health-related predictors significantly enhanced the model’s explanatory power beyond the sociodemographic–academic factors (p<0.001).

Among the health-related characteristics, adherence to the Mediterranean diet and physical activity emerged as the strongest predictors of a good HPLP-II score after adjustment for confounders. The participants with medium (AOR=0.30; 95% CI=0.19–0.49; p<0.001) and low (AOR=0.06; 95% CI=0.02–0.20; p<0.001) adherence to the Mediterranean diet were significantly less likely to achieve a good HPLP-II score than those with high adherence. Similarly, the participants with moderate (AOR=0.52; 95% CI=0.32–0.85; p=0.008) and low (AOR=0.37; 95% CI=0.17–0.80; p=0.012) physical activity levels were less likely to achieve a good HPLP-II score than those with high physical activity levels.

Regarding the sociodemographic–academic variables, the participants who were exclusively studying were also less likely to obtain a good HPLP-II score than their peers who were both studying and working (AOR=0.56; 95% CI=0.32–0.98; p=0.042).

4. Discussion

This is the first study in Spain to assess health-promoting lifestyle behaviors among nursing students using the HPLP-II and to identify associated factors. The findings revealed a moderately high or good level of engagement in health-promoting behaviors among the participants. The overall mean HPLP-II score was 2.62, placing the Spanish sample among the highest reported in the literature. This is slightly below scores from Indonesia (2.79) [31], the United States of America (USA) (2.76) [32], and Palestine (2.66) [14] but above those from Kuwait (2.6) [33], China (2.56) [15], Iran (2.55) [34], and India (2.53) [35]. In Türkiye, scores declined over time from 2.61 [36] to 2.57 [16], 2.46 [37,38], and 2.36 [39]. Lower scores were also reported in South Korea (2.47) [10], Lebanon (2.46) [40], and Hong Kong (2.23) [41]. Notably, the mean score was markedly skewed toward the moderate HPLP-II range (91–129/1.74–2.48), highlighting the need to strengthen education and support to promote healthy lifestyles among nursing students.

In the present study, the highest adjusted mean subscale scores were observed in interpersonal relations (3.12) and spiritual growth (2.87). These findings are consistent with those of recent studies (10, 15, 31-35, 41). Internationally, interpersonal relations consistently ranked high among nursing students, reflecting strong social connections. Interpersonal factors, such as social norms, peer support, and role models, influence health behaviors [36] and are essential for effective health education [42]. They also play a key role in understanding patients and managing emotions [43]. Despite differences across regions and education systems, nursing students tend to develop supportive relationships and inner resources that sustain their well-being, likely strengthened by the collaborative nature of nursing training [39]. Similarly, spiritual growth was a prominent dimension across all countries, highlighting its universal role as a coping mechanism and source of internal motivation during the challenging nursing education process [44,45]. Notably, in this study, spiritual growth showed the strongest positive correlation with both interpersonal relations and overall health-promoting lifestyle, highlighting their close connection in influencing health behaviors and suggesting they should be addressed together. This is particularly relevant in clinical settings, where nurses are also responsible for providing spiritual care during illness [46].

Nutrition scored relatively high in this study (2.72), indicating that Spanish nursing students generally maintain favorable dietary habits. Internationally, nutrition was often ranked third and rated as moderate or high among the subscales. For example, students in Lebanon have the same score and ranking as the Spanish students (2.72) [40], while students in Palestine exhibit a slightly higher score (2.82) [14]. Nevertheless, although at the same rank, the Spanish scores are higher than those in Türkiye (2.63) [38] and (2.51) [16], the USA (2.62) [32], Iran (2.59) [34], China (2.50) [15], and Hong Kong (2.32) [41]. This strong performance likely reflects the influence of the Mediterranean diet, well known for its health benefits and cultural significance in Spain, supported by public health initiatives [47]. Additionally, 48.5% and 47.3% of our participants showed medium and high adherence to the Mediterranean diet, respectively, based on the KIDMED index.

By contrast, the lowest scores were observed in physical activity (2.47), stress management (2.33), and health responsibility (2.20). Consistently, 9.5% of the nursing students had a low physical activity level, suggesting challenges in maintaining preventive behaviors and coping strategies that support physical and psychological well-being. This pattern is not unique to Spanish students; it has also been reported in other nursing populations. For instance, in the USA, these three domains likewise ranked the lowest, although with slightly higher scores (physical activity: 2.59; stress management: 2.53; health responsibility: 2.49) [32]. Similarly, most international studies have reported low scores in stress management, health responsibility, and physical activity [14,15,16,34,38,40,41].

Despite the evidence documenting the benefits of physical activity [48] and a healthy lifestyle, a global trend of students not prioritizing physical activity and responsibility for health in their lifestyle has been observed [49]. These findings highlight a robust and recurring pattern across diverse cultural contexts, where a lack of exercise is a common habit among university students [50]. However, stress management and health responsibility emerged as the lowest-scoring domains. The inadequate adoption of stress management strategies likely reflects the high stress inherent to nursing education, encompassing academic demands, examinations, and fear of failure [51]. Clinical environments further intensify this burden through placement challenges, fear of errors, and professional interactions [52]. Likewise, diminished health responsibility suggests a tendency to prioritize patient care over personal well-being, as previously reported [53]. Furthermore, this study found strong positive correlations between health responsibility and stress management and between physical activity and nutrition. Embedding strategies such as short exercise breaks during academic schedules may not only reduce stress but also foster healthier lifestyle habits, including improved nutrition.

The hierarchical logistic regression analysis of the factors associated with the HPLP-II score revealed that the students’ health-related characteristics significantly predicted their health-promoting behaviors when controlling for the sociodemographic and academic variables. The inclusion of these health-related characteristics underscores the importance of strengthening such factors among nursing students to encourage healthy behaviors. However, the best-fitting model, which incorporated both sociodemographic–academic and health-related variables, explained only 24.5% of the variance in the health-promoting lifestyle behaviors.

Consistent with previous studies, this study did not find any significant differences in the HPLP-II score between the BMI groups (underweight, normal, overweight, and obese) [35,39,40]. Similarly, previous experience in the health field, participation in university wellness activities, the presence of a chronic disease, smoking habits, sleep patterns, and screen-viewing behaviors were not significantly associated with the health-promoting behaviors, except for adherence to the Mediterranean diet and level of physical activity. In this regard, this study further emphasizes the importance of regular physical activity and adherence to the Mediterranean diet as a strategy to improve health-promoting lifestyle behaviors among nursing students. Physical activity emerged as a strong determinant in other similar studies of nursing students [35,40]. However, to the best of our knowledge, no study has examined nutritional habits or adherence to the Mediterranean diet as a predictor of health-promoting lifestyle behaviors among nursing students.

Although nursing students are well educated on the importance and benefits of physical activity for health promotion, disease prevention, and disease management, a gap remains in their own self-care practices, particularly regarding exercise. This pattern was consistently observed internationally [10,34] and may be attributed to various personal and environmental factors, including demanding academic and clinical rotation schedules, poor time management, socioeconomic challenges, and limited social or institutional support for self-care [33,37,39]. Nursing students should be supported in identifying the underlying reasons for their low physical activity [39]. These findings highlight the need to move beyond theoretical instruction and instead emphasize action-oriented learning and the creation of a supportive environment that promotes healthy nutritional habits and regular physical activity throughout students’ academic journey.

Finally, consistent with our findings, Kara and İşcan found that the presence of a health problem and smoking habit did not emerge as determinants of health-promoting lifestyle behaviors [36]. The students in this study may not have perceived disease as a threat, and their health-promoting behaviors remained unaffected.

Regarding sociodemographic variables, employment status was a significant predictor of the health-promoting behaviors among the nursing students. Working students may be more likely to adopt health-promoting behaviors, possibly due to increased career maturity [54], personal responsibility, and self-awareness gained through professional and academic experiences. These factors collectively support healthier lifestyle choices [55] by increasing students’ health awareness and ability to manage it effectively [56]. Financial independence may also facilitate access to health-related activities and reflect a greater prioritization of well-being, either to sustain job performance or as part of a broader process of self-actualization [41]. However, contrarily to our result, no relationship emerged for employment status in a comparable study [40].

The current study did not find any sex difference in the overall HPLP-II score among the nursing students, in concordance with other reports [39,40]. However, other studies have shown disparities associated with this variable [10,35].

Consistent with previous studies, this study did not find a significant association of the HPLP-II score with age [35,39,40] or year of study [10,40]. However, our descriptive analysis showed that the 4th-year students had higher total HPLP-II scores than the other students. Spending more years in nursing education has been associated with improved health-related behaviors [57], as students develop a stronger sense of health responsibility through exposure to health-promoting content [10,37]. However, such knowledge and responsibility may still be insufficient to fully support consistent engagement in health-promoting behaviors [53,55] and self-care.

Finally, this study found no significant association between the family-related variables examined—such as maternal and paternal educational levels, living in the parental home, and family structure—and health-promoting behaviors. In line with previous studies, no significant relationship was also identified between the health-promoting behaviors and place of residence [35,36] or habitual residence [40].

Limited development of health-promoting behaviors in the family setting, underrepresentation in school curricula, predominantly disease-oriented university programs [38], and barriers such as fatigue, time constraints, and lack of institutional support can hinder participation in health-promoting behaviors [58]. However, if nursing students can identify the factors that hinder their own health-promoting behaviors and learn to manage them, they will be better equipped to encourage and support patients in adopting similar behaviors in the future [53].

Implications to Nursing Education and Practice

Although nursing curricula across Europe and globally include theoretical content on health promotion, translating this knowledge into personal practice remains a challenge for students. Nursing education should incorporate programs that not only deliver knowledge but also empower students through creative strategies to actively engage in health-promoting behaviors. While health-promoting behaviors are expected to improve with age and training, tailored interventions for each academic year may be necessary due to varying academic and clinical demands.

As universities offer accessible infrastructure and the potential to engage large numbers of adolescents, this study provides valuable guidance for university administrators, academic staff, and nursing curriculum planners in designing effective strategies that empower nursing students to adopt healthy behaviors and participate actively in health-promoting initiatives, including serving as peer educators.

Given the paucity of research on the factors influencing healthy behaviors among nursing students in Spain, the findings of this study may contribute to the development of culturally sensitive strategies aimed at enhancing such behaviors. Establishing supportive environments that encourage healthy lifestyles such as physical activity, balanced nutrition, and other positive habits may better prepare students to adopt these behaviors and effectively promote health in their future professional roles.

Future research should explore how additional environmental, behavioral, psychological, academic, and institutional factors influence health behaviors among nursing students. Key aspects may include family and cultural norms, time management, health literacy, stress and coping mechanisms, self-efficacy, motivation, exposure to nursing role models, clinical experiences, institutional support, and access to health-related resources. Conducting qualitative research to identify barriers and facilitators of health-promoting lifestyles is also essential for enhancing the explanatory power of predictive models and achieving a more comprehensive understanding of the determinants influencing nursing students’ health behaviors.

Study Limitations

This study has several limitations. Its cross-sectional design prevented causal inference and the assessment of changes in health-promoting behaviors over time. The use of a self-administered questionnaire may have introduced self-reporting and social desirability biases. Additionally, data collection at a single institution and the use of convenience sampling might have limited generalizability and reduced the representativeness of the findings. Future research should adopt longitudinal and multicenter designs to gain a deeper understanding of the evolution and broader patterns of health-promoting behaviors among nursing students.

Despite these limitations, the study provides a valuable and in-depth assessment of the HPLP-II score and its associations with a wide range of sociodemographic, academic, and health-related variables. With a robust hierarchical logistic regression model, the findings contribute meaningfully to the existing literature and highlight critical areas for targeted intervention. The high response rate in the study (93.43%) minimizes the risk of selective non-participation by nursing students who may have experienced greater barriers to adopting health-promoting lifestyles, thereby strengthening the validity and robustness of the findings. Furthermore, the study demonstrated excellent reliability of the HPLP-II scores within the population of nursing students in Spain.

5. Conclusions

In this study, the majority of the nursing students exhibited moderate-to-good levels of health-promoting behaviors. Among the subdomains, interpersonal relations and spiritual growth received the highest scores, while health responsibility and stress management scored the lowest. Notably, spiritual growth was strongly correlated with interpersonal relations, stress management, and health responsibility. Health responsibility was also strongly correlated with stress management at the individual level, suggesting a personal influence on health-promoting behaviors.

The health-related variables showed stronger associations with health-promoting behaviors than the sociodemographic–academic variables. In particular, adherence to the Mediterranean diet and engagement in physical activity as health-related factors—along with employment status as a sociodemographic variable—were significantly associated with health-promoting behaviors.

These findings underscore the importance of strengthening the nursing curriculum and training environment to more effectively promote nutrition, physical activity, and related health-promoting behaviors. Empowering nursing students in this manner will better equip them to manage their own health, serve as role models for healthy lifestyles, effectively educate the public in their future professional roles, and contribute to the long-term sustainability of the nursing profession.