Influence of smart device use,online health information seeking,and self-perceived health on health-promoting behaviors among university students

Abstract

Objectives

This study investigated the association of smart device usage—particularly smartwatch use—online health information utilization, and subjective health status on health-promoting behaviors among university students in South Korea. As digital technologies are increasingly integrated into daily routines, an individual’s capacity to access and apply health information using mobile and wearable devices represents an important factor related to healthy behaviors.

Methods

An online survey was administered to university students in urban settings to evaluate the associations among smart device usage patterns, utilization of online health information, subjective health status, and health-promoting behaviors. Collected data were subjected to descriptive statistics, correlation analysis, and multiple regression to determine the key factors associated with of health-promoting behaviors.

Results

The study found that increased utilization of online health information and more favorable subjective health status correlated with greater participation in health-promoting behaviors. Furthermore, both the frequency and duration of health information searches on smartphones, together with the main smartwatch functions utilized, demonstrated significant associations with these behaviors. The results underscore that proactive use of digital health tools is associated with enhanced personal health management.

Conclusions

The findings of this study underscore the significance of advancing digital health literacy and fostering efficient utilization of wearable devices such as smartwatches in relation to self-care and healthy living among university students. This evidence may support the development of targeted digital health promotion initiatives and policies designed for the modern digital environment.

Keywords

smart device health information seeking behavior health behaviors students self perceived health

Introduction

Background

Health behavior is fundamental to preventing chronic diseases and enhancing quality of life, while the adoption of a healthy lifestyle is shaped by individuals’ health perceptions and their engagement with external health information. With digital technologies now deeply embedded in daily routines, the capacity to access and interpret health information via mobile devices has emerged as a significant factor related to health behavior.

Smartphones facilitate access to health information through diverse avenues, such as information searches, health applications, and online self-diagnostic resources. These resources enable more proactive engagement in health-related practices as their use increases. Among wearable devices, smartwatches have recently emerged as the most prominent.¹ A smartwatch, categorized as a wearable device, serves not only as a timepiece but also offers various biometric measurements and feedback capabilities, and is characterized as a computer in the form of a watch, bracelet, or glasses that can be worn on the body.² In recent years, smartwatches have progressed from basic time-keeping functions to advanced wearable mini-computers that can collect and analyze individual health data.³

This evolution in technology has substantially been associated with changes in personal health management. The proliferation of wearable devices, including smartwatches, provides an effective platform for supporting healthy behaviors. By enabling the real-time collection of biometric data—such as step count, heart rate, sleep pattern, and calorie expenditure—smartwatches deliver immediate feedback. This supports self-regulation in health management and is associated with greater participation in health-promoting behaviors.^4,5 This research is conceptually based on Pender’s Health Promotion Model (HPM).⁵ Pender (1982)⁵ asserts that individual health behavior encompasses more than disease prevention. It is influenced by personal health perceptions, perceived benefits and barriers to action, and self-efficacy.

From this theoretical perspective, the utilization of online health information is associated with the perceived benefits of health management. The use of smart devices may support self-regulatory practices through continuous feedback, and perceived health status indicates the individual’s subjective health level. Accordingly, this study employed Pender’s model as the theoretical foundation and suggests that university students’ health-promoting behaviors may be associated with the dynamic interaction between cognitive determinants and digital environmental factors. Individuals who actively seek health information are more likely to utilize wearable devices, including smartwatches, which may be associated with greater adoption of health-promoting behaviors. In addition, wearable devices such as smartwatches have been identified as innovative and cost-effective psychological interventions that are associated with healthy behavioral changes, support health management, and may encourage behavioral modification.⁶ Furthermore, in everyday settings, smartwatches have the capacity to transform personal health management by enabling the monitoring and evaluation of individual health metrics.⁷

Meanwhile, subjective health status—the individual’s self-assessed perception of their own health—acts as a critical psychosocial variable that shapes the interplay between health information engagement and device usage. Although individuals may encounter identical health information, those holding a positive view of their health are more apt to process and implement that information, whereas individuals with a negative perception are more inclined to disregard or inadequately apply it in practice. Consequently, subjective health status not only is associated with health-promoting behaviors but may also serve as a moderating factor in the association between digital device utilization and the consumption of online health information.⁸

University students comprise a significant demographic for smartphone and wearable device usage, demonstrating strong familiarity with mobile-centric lifestyles and a high propensity for adopting emerging technologies, thus reflecting broader digital health trends.⁹ Additionally, the university period constitutes a pivotal phase of transition to adulthood, during which foundational health habits and self-management skills are developed.¹⁰ Health behaviors established in this stage may exert a lasting influence on health outcomes later in life, underscoring the importance of examining digital health-promoting behaviors among university students as a foundation for enhancing individual and population health.

Consequently, this study targeted university students to investigate the association of smartwatch usage and online health information engagement with the adoption of health-promoting behaviors. The study intends to contribute empirical insights supporting the creation of digital health promotion strategies that are customized to the unique health management needs of younger populations in the digital age.

By analyzing the collective associations of smartwatch usage, online health information engagement, and subjective health status on health-promoting behaviors, this research seeks to supply essential data for the development of effective health promotion initiatives and targeted digital health interventions.

Objectives

This study aimed to examine the associations between smart device usage, online health information utilization, subjective health status, and health-promoting behaviors among university students in South Korea and to identify factors associated with health-promoting behaviors.

Materials and methods

Research design

This study was reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies. This cross-sectional study sought to examine the associations between smart device usage, online health information-seeking behaviors, and perceived health status with health-related practices among university students residing in urban regions of South Korea. The study also aimed to generate foundational data to facilitate self-managed health promotion in young adults.

Participants and setting

Data were collected over the period from August 1 to September 1, 2025. The study participants consisted of university students registered at three higher education institutions in Daejeon Metropolitan City, Gyeonggi-do, and Chungcheongnam-do. The survey was distributed via an online platform, allowing participants to complete the questionnaire at their convenience in any location. The survey link was distributed through university communication channels (e.g., online student communities and internal announcements), and participation was voluntary.

The sample size calculation was performed using the G*Power 3.1.9.7 program.¹¹ Using a significance threshold of α = .05, statistical power (1–β) = .95, and effect size = .15 for 13 predictors, the minimum sample size needed was 189. To account for potential attrition, the final study cohort included 210 participants.

The investigator provided an online explanation of the study’s aims and methodology and invited students to participate in the electronic survey on a voluntary basis. A total of 210 responses were included in the final analysis; incomplete or invalid responses were excluded.

Inclusion criteria required participants to: (1) be undergraduate students aged 19 years or older, currently enrolled at a university in South Korea; (2) possess a smartphone and have the ability to complete an online survey using a digital device; and (3) comprehend the study objectives and voluntarily agree to participate with informed consent.

Exclusion criteria were as follows: (1) submission of incomplete questionnaires or patterned responses, such as having significant missing data or providing identical answers to multiple items; (2) lack of sufficient understanding of the study’s aims; and (3) withdrawal from the study before survey completion.

Participants were notified that involvement was voluntary, withdrawal was permitted at any stage without repercussions, and all collected data would be kept anonymous. Potential sources of bias related to self-reported data and convenience sampling were considered during the study design and analysis. Incomplete or patterned responses were excluded from the analysis.

Research validation and research measurement

Reliability refers to the stability and reproducibility of research results, while validity denotes how accurately the findings reflect the actual phenomena being studied. To strengthen both reliability and validity, this study incorporated quantitative data collected through systematic survey analysis and employed stringent protocols for data acquisition and examination.

The instruments utilized in this research demonstrated previously established reliability and validity. All instruments used in this study were previously validated in prior research and were applied without substantial modification for the target population.

Specifically, to support reliability, the internal consistency of the survey instruments was analyzed, and measurement tools with Cronbach’s alpha coefficients of 0.80 or greater were selected. Additionally, content validity was assessed via expert evaluation. Two nursing professionals reviewed the survey items for relevance and clarity. The measurement tools applied in this study are specified as follows.

General characteristics

The section on general characteristics comprised five items: age, gender, academic year, disease status, and type of residence.

Characteristics of smart device use

Characteristics of smart device usage were evaluated using various indicators: years of smartphone ownership, frequency of searching for health information via smartphone (classified as “daily,” “1–2 times per week,” “once per month,” “once every 2–3 months,” or “Do not search”), self-reported minutes spent searching for health information using a smartphone, smartwatch use (yes/no), and primary smartwatch functions used (such as physical activity monitoring, tracking sleep or heart rate, and notifications for alarms or messages).

The duration of smartphone use was categorized into two groups: less than 10 years and 10 years or more. The frequency of smartphone-based health information searches was classified into five categories: daily, 1–2 times per week, once a month, once every 2–3 months, and never. Hours devoted to searching for health information using a smartphone were categorized into three groups: less than 1 hour, 1 to less than 2 hours, and 2 hours or more.

Smartwatch usage was classified into two categories: yes and no. The most frequently used smartwatch function was categorized into three groups: step count/running tracking, sleep/heart rate monitoring, and alarm, messages, and calling. Categorical variables were dummy-coded for multiple regression analysis, and the reference categories are presented in Table 1.

Table 1.

Coding of categorical variables for multiple regression analysis.

Variable	Dummy variable	Coding	Reference category
Frequency of smartphone-based health information searches	Daily	1 = daily, 0 = otherwise	Do not search
	1–2 times per week	1 = 1–2 times per week, 0 = otherwise	Do not search
	Once per month	1 = once per month, 0 = otherwise	Do not search
	Once every 2–3 months	1 = once every 2–3 months, 0 = otherwise	Do not search
Hours spent searching for health information using a smartphone	1-<2	1 = 1 to < 2 hours, 0 = otherwise	< 1 hour
	≥2	1 = ≥ 2 hours, 0 = otherwise	< 1 hour
Most frequently used smartwatch function	Step count/Running tracking	1 = step count/running tracking, 0 = otherwise	Alarm, messages, and calling
Most frequently used smartwatch function	Sleep/heart rate monitoring	1 = sleep/heart rate monitoring, 0 = otherwise	Alarm, messages, and calling

Online health information utilization

Online health information utilization was measured using the instrument developed by Yu¹² to assess online health information utilization. The instrument includes items on whether participants seek information online about the causes and treatment of their illnesses, exercise methods, medications, and dietary therapy.

The questions are designed to focus on the participants’ behaviors. The instrument consists of a total of 16 items. The scale comprised 16 items on a 7-point Likert scale. Higher total scores reflected greater engagement with online health information. Yu¹² reported a reliability of Cronbach’s α = .87 for this instrument. In the current study, its Cronbach’s α was .93.

Subjective health status

Subjective health status was measured using the Subjective Health Status Scale, originally designed at Northern Illinois University¹³ and subsequently translated and modified by Shin and Kim.¹⁴ This instrument included three items, each rated on a 5-point Likert scale. Higher average scores represented better perceived health. Shin and Kim¹⁴ reported the reliability of this scale as Cronbach’s α = .70. In this study, Cronbach’s α was .84.

Health-promoting behaviors

Health-promoting behaviors were measured using the Health Promoting Lifestyle Profile II (HPLP-II), originally developed by Walker, Sechrist, and Pender¹⁵ and later translated and culturally adapted for Korean populations by Seo.¹⁶

The tool consists of six subscales with a total of 52 items: health responsibility (9 items), physical activity (8 items), nutrition (9 items), spiritual growth (9 items), interpersonal relations (9 items), and stress management (8 items). Each item is assessed using a 4-point Likert scale from 1 (“never”) to 4 (“routinely”). Higher mean scores reflect greater engagement in health-promoting behaviors.

The internal consistency reported by Seo¹⁶ was Cronbach’s α = .92. In the current study, Cronbach’s α = .93.

Statistical methods

The data were analyzed utilizing IBM PASW Statistics (SPSS) version 25.0. This study recruited university students from urban regions of South Korea to evaluate smart device usage—specifically smartwatch use—patterns of online health information utilization, subjective health status, and health-promoting behaviors, as well as the relationships between these variables.

The study adopted a descriptive design to determine factors associated with health-promoting behaviors. Continuous variables were analyzed using correlation and multiple regression analyses. Cases with incomplete responses were excluded from the analysis to minimize the impact of missing data. The analytical procedures were as follows:

(1) The general characteristics and smart device usage characteristics of participants were analyzed using frequency and percentage.

(2) Mean and standard deviation were calculated to analyze the levels of online health information utilization, subjective health status, and health-promoting behaviors.

(3) The chi-square test, t-test, ANOVA, and Scheffé test were used to determine differences in online health information utilization, subjective health status, and health-promoting behaviors according to general characteristics.

(4) To assess differences in online health information utilization, subjective health status, and health-promoting behaviors based on smart device usage characteristics, the chi-square test, t-test, ANOVA, and Scheffé test were applied.

(5) Correlation analysis was conducted to evaluate the relationships among online health information utilization, subjective health status, and health-promoting behaviors.

(6) Multiple linear regression was performed to identify factors influencing health-promoting behaviors. Prior to regression, model assumptions were assessed for adequacy.

Multicollinearity was checked using the Variance Inflation Factor (VIF). The normality, homoscedasticity, and independence of residuals were verified through standardized residuals and the Durbin–Watson statistic.

Results

General characterisitcs

A total of 210 participants were included in the study. The general characteristics are described in Table 2. The mean age of participants was 23.96 ± 7.44 years, and the majority were aged between 20 and 30 years (87.1%, n = 183). Most participants identified as female (75.2%, n = 158). Sophomores made up the largest academic year group (50.0%, n = 105). Regarding health status, the majority of students reported no current disease (97.1%, n = 204). In relation to living arrangements, the largest proportion of students lived with friends (42.4%, n = 89).

Table 2.

General characteristics (N=210).

Characteristics	Categories	n(%) or M±SD
Age		23.96±7.44
	20-30	183(87.1)
	>30	27(12.9)
Gender	Female	158(75.2)
Gender	Male	52(24.8)
Academic year	1	75(35.7)
	2	105(50.0)
	3	4(1.9)
	4	26(12.4)
Disease status	With disease	6(2.9)
Disease status	Without disease	204(97.1)
Type of residence	Reside with parents	67(31.9)
	Live alone	54(25.7)
	Reside with a friend	89(42.4)

Smart device use characteristics

The smart device use characteristics of participants are summarized in Table 3. Most had used smartphones for over 10 years (78.6%, n = 165). In terms of health information searches on smartphones, 32.9% (n = 69) reported searching one to two times weekly. A majority (80.5%, n = 169) spent less than one hour during each session when searching for health information. Approximately half of the participants (50.5%, n = 106) indicated smartwatch usage. The step count or running tracking feature was reported as the most commonly used function on smartwatches by 40.0% (n = 84) of participants.

Table 3.

Smart device use characteristics (N=210).

Characteristics	Categories	n(%) or M±SD
Duration of smartphone usage (yr)	<10	45(21.4)
Duration of smartphone usage (yr)	≥10	165(78.6)
Frequency of smartphone-based health information searches	Daily	28(13.3)
	1–2 times per week	69(32.9)
	Once per month	51(24.3)
	Once every 2–3 months	35(16.7)
	Do not search	27(12.9)
Hours spent searching for health information using a smartphone	<1	169(80.5)
	1-<2	34(16.2)
	≥2	7(3.3)
Smartwatch usage	Yes	106(50.5)
Smartwatch usage	No	104(49.5)
Most frequently used smartwatch function	Step count/Running tracking	84(40.0)
	Sleep/heart rate monitoring	55(26.2)
	Alarm, messages, and calling	71(33.8)

Level of online health information utilization, smartphone-based health information use, subjective health status, and health-promoting behaviors

Table 4 displays the levels of online health information utilization, subjective health status, and health-promoting behaviors among participants. The mean score for online health information utilization was 4.57 ± 1.25. For subjective health status, the mean score was 3.60 ± 0.69, and for health-promoting behaviors, the mean was 2.46 ± 0.48.

Table 4.

Level of online health information utilization, subjective health status, and health-promoting Behaviors(N=210).

Variables	Mean±SD	Min	Max	Range
Use of online health information	4.57±1.25	1.00	7.00	1-7
Self-reported health status	3.60±0.69	1.33	5.00	1-5
Health-promoting behaviors	2.46±0.48	1.08	3.92	1-4

Differences in online health information utilization, subjective health status, and health-promoting behaviors according to general characteristics

Table 5 presents the differences in online health information utilization, subjective health status, and health-promoting behaviors based on the participants’ general characteristics. No statistically significant differences were observed in online health information utilization concerning any of the general characteristics. However, subjective health status showed significant differences depending on disease status (t = –2.610, p = .010) and type of residence (F = 3.507, p = .032). Health-promoting behaviors were significantly different according to gender.

Table 5.

Differences in online health information utilization, subjective health status, and health-promoting behaviors according to general characteristics (N=210).

Characteristics	Categories	Use of online health information		Self-reported health status		Health-promoting behaviors
Characteristics	Categories	M±SD	t or F (p)	M±SD	t or F (p)	M±SD	t or F (p)
Age	20-30	4.53±1.23	-1.071 (.319)	3.61±0.69	0.419 (.683)	2.44±0.46	-1.691
Age	>30	4.81±1.33	-1.071 (.319)	3.55±0.71	0.419 (.683)	2.61±0.57	(.092)
Gender	Female	4.41±1.25	-1.057 (.292)	3.76±0.07	1.866 (.063)	2.63±0.46	2.938
Gender	Male	4.62±1.24	-1.057 (.292)	3.55±0.71	1.866 (.063)	2.41±0.48	(.004)
Academic year	1	4.73±1.26	1.142 (.333)	3.63±0.52	0.559 (.643)	2.52±0.50	1.590 (.193)
	2	4.50±1.27		3.57±0.82		2.47±0.49
	3	3.75±1.01		4.00±0.00		2.30±0.69
	4	4.48±1.03		3.61±0.57		2.29±0.36
Disease status	With disease	3.64±0.39	-1.856 (.065)	2.88±1.24	-2.610 (.010)	2.82±0.59	1.825 (.069)
Disease status	Without disease	4.59±1.25	-1.856 (.065)	3.62±0.66	-2.610 (.010)	2.45±0.47	1.825 (.069)
Type of residence	Reside with my parents	4.53±1.10	0.605 (.547)	3.82±0.46	3.507 (.032)	2.53±0.44	1.303 (.274)
	Reside alone	4.44±1.43		3.53±0.81		2.48±0.46
	Reside with a friend	4.67±1.22		3.53±0.69		2.40±0.52

Differences in online health information utilization, subjective health status, and health-promoting behaviors according to smart device use characteristics

Table 6 presents differences according to smart device use characteristics. Online health information utilization varied significantly by the frequency of using a smartphone to search for health information (F = 9.332, p < .001).

Table 6.

Differences in online health information utilization, subjective health status, and health-promoting behaviors by smart device use characteristics (N=210).

Characteristics	Categories	Use of online health information		Self-reported health status		Health-promoting behaviors
Characteristics	Categories	M±SD	t or F (p)	M±SD	t or F (p)	M±SD	t or F (p)
Duration of smartphone use (yr)	<10	4.59±1.16	0.137 (.892)	3.85±0.52	2.699 (.008)	2.52±0.41	0.927 (.355)
Duration of smartphone use (yr)	≥10	4.56±1.26	0.137 (.892)	3.54±0.71	2.699 (.008)	2.44±0.50	0.927 (.355)
Frequency of smartphone-based health information searches	Daily^a	5.11±1.13	9.332 (<.001) b>e	3.58±0.85	2.514 (.043) a>e	2.73±0.49	6.183 (<.001) a>e
	1–2 times per week^b	4.53±1.26		3.86±0.57		2.56±0.44
	Once per month^c	4.62±1.13		3.70±0.54		2.38±0.47
	Once every 2–3 months^d	4.18±1.22		3.46±0.74		2.37±0.33
	Do not search^e	3.61±1.24		3.46±0.69		2.19±0.58
Hours spent searching for health information using a smartphone	<1^a	3.23±1.75	5.336 (.005) b>a	3.63±0.63	1.725 (.181)	2.23±0.88	12.137 (<.001) b>a
	1-<2^b	4.88±1.19		3.57±0.91		2.81±0.48
	≥2^c	4.56±1.20		3.14±0.74		2.40±0.43
Use of smartwatch	Yes	4.69±1.22	1.440 (.151)	3.56±0.73	-0.949 (.344)	2.50±0.50	1.063 (.289)
Use of smartwatch	No	4.44±1.25	1.440 (.151)	3.65±0.64	-0.949 (.344)	2.43±0.46	1.063 (.289)
Most frequently utilized smartwatch function	Step tracking/Running monitoring^a	4.75±1.29	1.480 (.230)	3.51±0.83	1.163 (.315)	2.63±0.50	9.951 (<.001) a>c
	Monitoring of sleep/heart rate^b	4.42±0.98		3.64±0.49		2.37±0.44
	Alarm, messaging, calling^c	4.47±1.35		3.68±0.63		2.31±0.42

Post-hoc analysis using the Scheffé test identified significant differences. These differences were observed between students searching one to two times per week and those who never searched. Moreover, there were significant differences according to the average time spent per search (F = 5.336, p = .005), Scheffé’s test confirmed differences between individuals who searched for one to two hours and those who searched for less than one hour. Subjective health status was significantly associated with the total duration of smartphone use (t = 2.699, p = .008). Additionally, significant differences in subjective health status were noted depending on the frequency of smartphone-based health information searches (F = 2.514, p = .043), with post-hoc tests identifying differences between students who searched daily and those who never searched.

Health-promoting behaviors differed significantly according to the frequency of health information searches via smartphone (F = 6.183, p < .001); the Scheffé test showed significant differences between daily searchers and those who never searched. Further, significant differences were found based on the average time spent per search (F = 12.137, p < .001), as indicated by Scheffé’s test, with students searching for one to two hours differing from those searching less than one hour.

Finally, the most frequently used smartwatch function also yielded significant differences (F = 9.951, p < .001). Scheffé’s test revealed that students using step count or running tracking features scored higher than those who mainly used alarm, message, or calling functions.

Correlation among online health information utilization, subjective health status, and health-promoting behaviors

Table 7 presents the correlations among online health information utilization, subjective health status, and health-promoting behaviors. There was a significant positive correlation between online health information utilization and health-promoting behaviors (r = .366, p < .001). Subjective health status also showed a positive correlation with health-promoting behaviors (r = .197, p = .004).

Table 7.

Correlation among online health information utilization, subjective health status, and health-promoting behaviors (N=210).

	Use of online health information	Self-reported health status	Health-promoting behaviors
	r (p)	r (p)	r (p)
Online health information utilization	1
Subjective health status	-.041 (.558)	1
Health-promoting behaviors	.366 (<.001)	.197 (.004)	1

Factors associated with health-promoting behaviors

A multiple regression analysis was conducted to examine factors associated with health-promoting behaviors (Table 8).

Table 8.

Factors affecting health-promoting behaviors (N=210).

Variables	B	SE	β	t	p	VIF
(Constant)	1.105	0.193		5.741	<.001
Online Health Information utilization	0.109	0.024	0.281	4.470	<.001	1.243
Subjective Health Status	0.172	0.041	0.246	4.214	<.001	1.073
Frequency of smartphone-based health information searches
Daily	0.291	0.111	0.205	2.624	.009	1.918
1–2 times per week	0.132	0.097	0.128	1.353	.177	2.810
Once per month	0.041	0.098	0.036	0.421	.675	2.359
Once every 2–3 months	0.067	0.104	0.051	0.641	.523	2.030
Hours spent searching for health information using a smartphone
1-<2	0.332	0.077	0.253	4.301	<.001	1.087
≥2	-0.071	0.161	-0.026	-0.442	.659	1.122
Most frequently utilized smartwatch function
Step count/Running tracking	0.242	0.065	0.245	3.707	<.001	1.381
Sleep/heart rate monitoring	-0.040	0.071	-0.036	-0.556	.579	1.330
R²=.368, Adjusted R²=.336(33.6%), F=11.587, p<.001 Durbin-Watson=2.028

*Note. Dummy variables included in the analysis (Refer to Table 1. Frequency of smartphone-based health information searches, Hours spent searching for health information using a smartphone, Most frequently utilized smartwatch function).

Variables that showed statistically significant differences in health-promoting behaviors in the preliminary analyses (t-test and ANOVA)—including the frequency of smartphone-based health information searches, the average time spent per search, and the most frequently used smartwatch function—were dummy-coded and entered as independent variables.

Online health information utilization and subjective health status were also included in the model. However, potentially relevant confounding variables, such as socioeconomic status, baseline physical activity, and mental health, were not included in the model. The overall regression model was statistically significant (F = 11.587, p < .001) and explained 36.8% of the variance in health-promoting behaviors (R² = .368; adjusted R² = .336).

The Durbin–Watson statistic was 2.028, indicating no evidence of autocorrelation among the residuals. Multicollinearity was not observed, with variance inflation factors (VIFs) ranging from 1.073 to 2.810.

Online health information utilization (β = .281, p < .001) and subjective health status (β = .246, p < .001) were significantly associated with health-promoting behaviors. Participants who searched for health information daily using a smartphone showed significantly higher levels of health-promoting behaviors compared with the reference group (β = .205, p = .009). The categories of 1–2 times per week, once per month, and once every 2–3 months were not statistically significant.

Participants who spent 1–2 hours per search reported significantly higher levels of health-promoting behaivors than the reference group (β = .253, p < .001), whereas those who spent two hours or more did not show a significant difference.

Individuals who most frequently used the step-counting or running-tracking function on a smartwatch reported significantly higher levels of health-promoting behaviors compared with the reference category (β = .245, p < .001), while the sleep or heart-rate monitoring function was not statistically significant.

Discussion and conclusion

Discussion

This study thoroughly examined the associations between smart device usage, online health information engagement, and individual health perceptions on health-promoting behaviors among university students. Contemporary university students are deeply integrated within digital environments, and the widespread adoption of wearable devices such as smartphones and smartwatches has been associated with changes in personal approaches to health management.⁷ Smartwatches offer immediate biometric feedback, including step count, heart rate, and sleep quality, empowering users to monitor and adjust their health-related routines.

This process may be considered an important mechanism associated with the adoption of health-promoting behaviors.³ Likewise, accessing health information via smartphones not only increases the availability of relevant resources but also may be associated with greater health awareness and confidence in managing their well-being.⁵ Therefore, the degree of active engagement with online health information may be related to the prevalence of health-promoting behaviors.

The results of this study demonstrated that increased utilization of online health information was associated with higher levels of health-promoting behaviors. This finding suggests that engagement with digital health information may play a meaningful role in shaping health-related behaviors among university students, particularly in digitally mediated environments.

These findings are consistent with previous research demonstrating that higher levels of eHealth literacy are associated with healthier lifestyle and health-related behaviors.¹⁰ Recent studies have similarly reported that digital health literacy is significantly associated with individuals’ engagement in health-related behaviors and their ability to utilize health information effectively in digital environments.¹⁷ Of note, students who frequently engaged in health-related searches on smartphones and devoted more time per inquiry tended to participate more actively in health-promoting activities. This pattern suggests that digital information-seeking serves not only as a means of acquiring health knowledge but may also be associated with the implementation of self-management strategies. This may indicate that the quality and depth of engagement with health information, rather than mere access, are critical factors influencing behavioral outcomes. The data highlight a transition among university students from passive health maintenance to a more proactive approach to self-care.

Notable differences also emerged based on specific smartwatch usage patterns. Individuals who primarily utilized step-counting and exercise-tracking applications exhibited significantly higher scores in health-promoting behaviors compared to those whose usage centered on functions such as timekeeping, messaging, or alarms. This finding suggests that behavior-specific and feedback-oriented functions may be more closely associated with active health management than passive or convenience-based uses of smart devices. Behavioral feedback theory may account for these findings.

Previous research has demonstrated that real-time feedback from wearable devices have been associated with increases in physical activity by over 25%,¹⁸ corroborating the current study’s outcomes. Smartwatches operate as tools for self-monitoring, which may enable users to identify and adjust their habits, with both the frequency and quality of feedback being associated with the adoption of healthful behaviors. Recent systematic reviews have also shown that wearable devices are associated with increased physical activity and improved health-related outcomes.¹⁹ In contrast, smartwatch functions such as notifications or alarms were not significantly associated with health-promoting behaviors, consistent with earlier studies indicating that reminder-based usage is typically ineffective in changing behavior.²⁰ These observations imply that the primary motive for smartwatch use—whether for convenience or for health-related monitoring— may be related to differences in the extent of health-promoting behaviors.

Moreover, the observation that subjective health status is positively associated with health-promoting behaviors suggests that individuals’ self-perceptions may be related to behavioral change. This may reflect the role of cognitive appraisal in influencing health-related decision-making and engagement. Several studies among young adults have demonstrated a significant association between seeing oneself as healthy and increased participation in physical activity and adherence to healthy eating patterns.²¹ In the current study, both the utilization of online health information and subjective health status were found to be important factors associated with health-promoting behaviors, highlighting the significance of both digital access to health information and individual health perceptions as factors related to behavior in digital settings. The observed association of online health information utilization (β = .281) is consistent with previous studies suggesting that higher levels of digital health literacy are associated with greater engagement in health-related behaviors.¹⁰ Similarly, the association of subjective health status (β = .246) suggests that individuals who perceive their health more positively tend to report higher levels of health-promoting behaviors. A favorable view of one’s own health may be associated with higher intrinsic motivation and accountability for health maintenance, whereas unfavorable self-perceptions are associated with greater avoidance behaviors. Therefore, efforts to foster health-promoting behaviors should include not only the dissemination of objective information but also psychosocial strategies aimed at enhancing health perception.

Regarding smartphone-based health information searches, the findings indicated that the frequency of searches was not consistently associated with health-promoting behaviors, whereas the time spent per search showed a positive association. Specifically, participants who reported spending 1–2 hours searching for health information demonstrated higher levels of health-promoting behaviors. This suggests that sustained engagement with health information, rather than frequency alone, may be more closely associated with behavioral outcomes. This pattern suggests that the depth and engagement of information seeking, rather than search frequency alone, may be more closely related to health behavior outcomes. Allocating sufficient time to explore and understand health information may therefore be associated with greater engagement in health-related behaviors.

Given that smartphone-based health information seeking is common among university students, these findings may indicate that the quality and depth of information engagement are important considerations when examining the relationship between digital health information use and health behaviors. Accordingly, university health education initiatives may benefit from emphasizing the development of critical information evaluation and eHealth literacy skills, rather than focusing solely on increasing the frequency of online health information searches.

With respect to smartwatch use, individuals who primarily used step-counting or running-tracking functions reported higher levels of health-promoting behaviors compared with the reference category. In contrast, sleep or heart-rate monitoring functions were not significantly associated with health-promoting behaviors. These findings suggest that the type of smartwatch function used may be associated with differences in health-promoting behaviors, particularly when the functions are directly related to physical activity monitoring. Previous studies have also suggested that wearable devices may support physical activity engagement by providing self-monitoring and feedback mechanisms.^3,18 Therefore, the relationship between smartwatch use and health behaviors may depend not only on device ownership but also on the specific purpose and type of functions utilized. The regression model accounted for 33.6% of the variance in health-promoting behaviors, demonstrating that these behaviors may be associated with a multifaceted interaction of psychological factors (such as health perception, self-efficacy) and social circumstances (including time management and academic stress), rather than being explained exclusively by digital device use. However, online health information utilization and subjective health status were identified as the most influential factors associated with health-promoting behaviors, suggesting that engagement with digital health resources may be related to healthy behaviors among young adults.

The results may provide useful insights for designing health promotion initiatives specifically suited for university students. As digital technology is deeply integrated into their daily lives, university students are especially open to technology-driven self-management strategies related to health-related outcomes.⁹ Incorporating smartwatch-facilitated physical activity tracking, education in online health information literacy, and feedback mechanisms that promote health awareness may be useful in the development of a comprehensive digital health initiative that supports participation in health-promoting activities. In summary, this study found that utilizing online health information and self-monitoring through smart devices were associated with subjective health perception, which was associated with health-promoting behaviors. Health promotion for university students within the digital health environment may be understood as an interactive process where information literacy, technology-enabled self-management, and a positive perception of health coexist. These findings highlight the potential importance of implementing digital health literacy education and health promotion programs that integrate smart devices within university and public health education contexts.

However, this study has several important limitations. Several limitations should be considered when interpreting these findings. First, the cross-sectional design precludes establishing causal relationships among the variables. Although significant associations were observed between smart device usage, online health information utilization, subjective health status, and health-promoting behaviors, the directionality of these relationships cannot be determined. Therefore, the results should be interpreted in terms of associations rather than causality. Second, participants were recruited using convenience sampling from three urban universities. This sampling approach may limit the representativeness of the sample and restrict the generalizability of the findings to the broader population of university students. This approach may limit the representativeness of the sample; therefore, caution should be exercised when generalizing the findings beyond similar urban university settings to the broader population of university students in South Korea. Digital device usage and health behaviors may vary significantly depending on regional characteristics or institutional environments. Third, the sample was predominantly female (approximately 75%), and Most participants reported relatively good health. These characteristics may have influenced the observed patterns of health-promoting behaviors and digital health information utilization, and may further limit the generalizability of the findings.

Accordingly, the findings should be applied with caution to populations with different gender distributions or health conditions. Fourth, Furthermore, several potentially important confounding variables, such as socioeconomic status, baseline physical activity, and mental health status, were not included in the regression model, which may have introduced omitted-variable bias and limited the robustness of the observed associations.

Finally, while this study provides preliminary insights, future research should incorporate more diverse and representative samples across various regions. Furthermore, longitudinal or multi-institutional designs would be valuable to verify whether these relationships persist across different contexts and over time, thereby enhancing the generalizability and reproducibility of the findings.

In conclusion, the current study identifies significant associations between engagement with online health resources, smart-device–mediated self-monitoring, and health-promoting behaviors among university students. Health promotion within the digital paradigm can be understood as an interactive process involving information literacy, technology-enabled self-management, and subjective health perception. These findings highlight the importance of integrating digital health literacy education and device-supported programs within university health promotion initiatives.

Consistent with previous literature indicating that wearable technologies may be associated with behavioral change through real-time feedback mechanisms, our findings suggest that digital literacy plays an important role in translating health information into daily health-related practices.^22,23 Accordingly, combining wearable-based health monitoring with targeted eHealth literacy education may be considered a potentially effective strategy for promoting sustainable health behaviors among university students in digitally connected environments.

Conclusion

This study examines the association between smart device usage, online health information engagement, and subjective health perceptions and health-promoting behaviors among university students. The findings underscore that active involvement with digital health resources and positive health self-perceptions are pivotal factors associated with health behaviors in digitally connected environments. Notably, the intentional use of digital technologies and wearable devices for health-specific purposes may be associated with self-directed health management among this population.

These results highlight the significance of digital health literacy and technology-assisted self-monitoring in understanding student health behaviors. However, due to the cross-sectional nature of this study, the observed relationships should be interpreted as associations rather than causal effects. Future research utilizing longitudinal designs and incorporating a broader range of confounding variables is warranted to further elucidate these dynamics and advance the understanding of digital health behaviors in young adults.

Footnotes

Acknowledgements

The authors have no acknowledgements to declare.

ORCID iD

In-Kyoung Kim

Ethical consideration

The Institutional Review Board of Hanseo University approved this study (IRB No. HS25-07-03). All participants provided electronic informed consent before participation and were informed that they could withdraw from the study at any time without penalty. Measures were taken to avoid potential conflicts of interest, as no participant was a student currently enrolled in the researcher’s classes. All data were anonymized, coded for analysis, and securely stored on a password-protected computer accessible only to the principal investigator.

Author contributions

In-Kyoung Kim contributed to the study conception, design, data collection, analysis, and manuscript writing.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Guarantor

In-Kyoung Kim is the guarantor of this work.

References

Yoon

Lee

Kim

, et al. Exploring the chasm in smartwatch market: Q-method study of non-adopters. J Inf Technol Serv 2019; 18(1): 27–44. https://doi.org/10.9716/KITS.2019.18.1.0272

Noh

. Portable health monitoring systems using wearable devices. Indian J Sci Technol 2016; 9(36): 1–5. https://doi.org/10.17485/ijst/2016/v9i36/102699

Piwek

Ellis

Andrews

, et al. The rise of consumer health wearables: Promises and barriers. PLoS Med 2016; 13(2): e1001953. https://doi.org/10.1371/journal.pmed.1001953

Kim

Lee

Woo

. Is heart rate measured by smartwatch during exercise reliable? Analysis of correlation and agreement between heart rates of Polar and smartwatch. J Korea Converg Soc 2020; 11(6): 331–339. https://doi.org/10.15207/JKCS.2020.11.6.331

Pender

. Health Promotion in Nursing Practice. Appleton-Century-Crofts, 1982.

Yen

Huang

. The efficacy of commercial smartwatches with a blood pressure-monitoring feature: A pilot randomized controlled trial. J Nurs Scholarsh 2022; 54(3): 324–331. https://doi.org/10.1111/jnu.12740

Reeder

David

. Health at hand: A systematic review of smartwatch uses for health and wellness. J Biomed Inform 2016; 63: 269–276. https://doi.org/10.1016/j.jbi.2016.09.001

Kang

Lee

. The relationship among subjective health status, social support and health-promoting behavior: Focusing on small manufacturing male workers. J Wellness 2017; 12(4): 413–421. https://doi.org/10.21097/ksw.2017.12.4.413

Madrid-Cagigal

Kealy

Potts

, et al. Digital mental health interventions for university students with mental health difficulties: A systematic review and meta-analysis. Early Interv Psychiatry 2025; 19(3): e70017. https://doi.org/10.1111/eip.70017

10.

Mitsutake

Shibata

Ishii

, et al. Associations of eHealth literacy with health behavior among adult internet users. J Med Internet Res 2016; 18(7): e192. https://doi.org/10.2196/jmir.5413

11.

Faul

Erdfelder

Buchner

, et al. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods 2009; 41(4): 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

12.

. Middle-aged's Internet health information usage research: Focused on the theory of uses and gratification [Master's thesis]. Chung-Ang University, 2011.

13.

Northern Illinois University . Measuring Health: A Guide to Rating Scales and Questionnaires. Northern Illinois University Press, 1989.

14.

Shin

Kim

. Health behaviors, health status, and anxiety about aging among the elderly: Focused on senior center participants. J Korean Gerontol Soc 2009; 20(2): 179–188.

15.

Walker

Sechrist

Pender

. The Health-Promoting Lifestyle Profile II. University of Nebraska Medical Center, 1995.

16.

Seo

. Construction of health promoting behaviors model in elderly [dissertation]. Seoul National University, 2001.

17.

Tang

Peng

, et al. The impact of digital literacy on health behaviors among middle-aged and older adults: the mediating roles of proactive health awareness and social capital. Front Public Health 2026; 14: 1735211. https://doi.org/10.3389/fpubh.2026.1735211

18.

Patel

Asch

Volpp

. Wearable devices as facilitators, not drivers, of health behavior change. JAMA 2021; 325(5): 431–432. https://doi.org/10.1001/jama.2020.25363

19.

Wang

, et al. Wearable activity tracker–based interventions for physical activity, body composition, and physical function among community-dwelling older adults: a systematic review and meta-analysis of randomized controlled trials. J Med Internet Res 2025; 27(1): e59507. https://doi.org/10.2196/59507

20.

Yuan

Wang

, et al. Effectiveness of digital health interventions in promoting physical activity among college students: Systematic review and meta-analysis. J Med Internet Res 2024; 26: e51714. https://doi.org/10.2196/51714

21.

Tavares

Oliveira

. eHealth literacy and health behavior among young adults: Cross-sectional study. JMIR Public Health Surveill 2020; 6(4): e18598. https://doi.org/10.2196/18598

22.

Free

Phillips

Watson

, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Medicine 2013; 10(1): e1001363. https://doi.org/10.1371/journal.pmed.1001363

23.

Patel

Asch

Volpp

. Wearable devices as facilitators, not drivers, of health behavior change. JAMA 2015; 313(5): 459–460. https://doi.org/10.1001/jama.2014.14781