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Abstract

Objective

This study implemented a 12-week walking program using the mobile app WalkON among college students and evaluated its effects.

Methods

This study used a quasi-experimental, non-equivalent control group, pre/post design. The WalkON program was conducted from September to December 2022, involving 50 participants in the experimental group and 52 participants in the control group.

Results

Significant between-group differences were observed as follows. Sleep quality evaluation score (i.e. higher scores mean poorer sleep quality) decreased more significantly in the experimental group (vs. control group; 1.40 vs. 0.34 points, respectively; p = .027). Anxiety decreased more in the experimental group than in the control group (2.08 vs. 1.75 points, respectively; p = .020). Regarding the mean score of the “health responsibility” domain of health-promoting behaviors, the experimental group saw an increase of 0.25 points compared to 0.15 in the control group (p = .005). The self-efficacy level increased by an average of 0.34 points in the experimental group and 0.03 points in the control group (p = .046).

Conclusions

The WalkON program has the potential to positively influence physical activity engagement and health-promoting behaviors in college students. It could be an effective strategy for promoting the physical and mental health of college students lacking engagement in physical activity. This study is significant in that it provides evidence of a mobile app-based program potentially useful to encourage college students to increase their regular physical activity through walking exercises, something especially pertinent in the new norm of contactless environments post-COVID-19.

Keywords

Walking program mobile app college students anxiety health promotion self-efficacy

Introduction

After the outbreak of the COVID-19 pandemic in 2020, college students underwent considerable lifestyle changes. For example, there was a decrease in participation in outdoor and physical activities owing to prolonged restrictions such as obligatory remote education and social distancing.¹ There was also a noticeable shift in dietary habits to irregular meals and increased consumption of processed foods, delivery options, and snacks. The combination of diet changes and reduced physical activity led to weight gain for many,¹ and in terms of mental health, college students showed high prevalence rates of stress (20.6%), anxiety (23.7%), depression (15.4%), suicidal ideation (13.8%), and post-traumatic stress disorder (29.8%).² The 2020 Korean National Health and Nutrition Examination Survey showed a marked increase in obesity rates among adults following the pandemic's outbreak: an increase of 6.2% for men (41.8% in 2019–48.0% in 2020) and 2.7% for women (25.0–27.7% in the same period).³ Moreover, a 2021 survey conducted by the Korean Obesity Society that focused on national weight management status post-COVID-19 revealed that 46% of respondents gained over 3 kg since the pandemic began.⁴ There was also the 2020 Community Health Survey, which was conducted during the peak of the pandemic and shed light on health behavior changes owing to COVID-19. The following are the notable changes reported in the results: smoking rate declined to 19.8% (the first-ever dip into the 10–20% range); monthly alcohol consumption saw a 5.2% reduction (54.7% in 2020); high-risk drinking rate decreased from 14.1% to 10.9%; adherence to walking exercise rate decreased from 40.4% in 2019 to 37.4% in 2020; non-compliance with moderate-to-high-intensity physical activity increased from 4.9% to 19.8%; and delivery food orders and instant food and carbonated beverages consumption rates surged by 38.5% and 21.5%, respectively, compared to the rates pre-COVID-19.⁵

The results of the 2021 COVID-19 National Mental Health Status Survey further underscored the profound mental health toll of the pandemic, especially among young adults in their 20s and 30s, as there was a significant and evident deterioration in mental well-being based on the surge in depression risk and suicidal ideation rates owing to COVID-19. For example, a comparison of 2021 Survey data and pre-COVID-19 data (sourced from the 2018 Community Health Survey) revealed that average depression scores more than doubled from 2.3 to 5.7, and the percentage of individuals at risk for depression increased 6-fold (3.8–22.8%). Regarding sex, both depression scores and the proportion of individuals at risk for depression were notably higher for women than men, with women in their 20s recording the highest depression score (at 7.1 points). The number of suicide deaths per 100,000 individuals in 2020 reached 25.7, with significant increases among teenagers (9.4%) and young adults in their 20s (12.8%).⁶

College students are entering adulthood—a phase when they can independently choose their lifestyle habits. These choices significantly influence their physical activity levels and health,⁷ and this period is pivotal for health-related habits as solid health habits have not yet taken root and thus health behaviors can be more readily modified.⁸ Health-promoting behaviors—as behaviors related to achieving optimal well-being, personal aspirations, and self-realization—are shaped by diverse factors, such as one's biological, social, and physical environments, lifestyle, and the healthcare system. Therefore, individual health beliefs (i.e. the degree of perception of oneself as healthy) are crucial in influencing health-promoting behaviors,⁹ and self-efficacy plays a direct role in health-promoting behavior adoption.¹⁰ More specifically, health beliefs offer cues for behavioral evaluations based on anticipated outcomes stemming from perceived risks of potential illnesses and subsequent health actions.¹¹ Moreover, health-related self-efficacy—one's confidence in successfully executing health behaviors—shapes various action-related psychological phenomena such as the determination to achieve goals, the intent to sidestep hazardous behaviors, and the engagement in tactics to surmount challenges.¹² In summary, health beliefs and self-efficacy are critical determinants of engagement in health-promoting behaviors.

Walking is the most accessible and typical low-impact aerobic exercise in our daily routines, playing a significant role in enhancing basic physical fitness and reducing body fat. Compared to running, walking exerts less strain on the musculoskeletal system and joints, reduces injury risk, and still offers significant health benefits. No wonder walking is advocated as an effective approach to health promotion for individuals across all age groups.^13,14 Beyond its potential in preventing adult-onset diseases like hypertension and diabetes, walking also offers therapeutic benefits for mental health conditions, including insomnia and depression.¹⁵

Moreover, in the post-COVID-19 landscape, contactless interactions have become normal in society, and leveraging digital platforms to establish physical activity routines is anticipated to be instrumental in shaping the future of healthcare systems.¹⁶ The WalkON mobile app—a walking-based community healthcare platform—emerged as a user-friendly tool to augment physical activity levels in the general population. It is one of the first services in South Korea to introduce the concept of providing rewards based on the user's number of steps. It informs users of daily step counts and activity duration, and it allows users to operate various activities through the app, such as form a community for sharing health-related useful information via bulletin boards and organizing desired challenges. It launched in 2015 and is operated by Swallaby (https://www.swallaby.com).

Social distancing owing to COVID-19 has increased the possibility of lack of physical activity because of the suppression of outdoor activities, suspension and avoidance of exercise facilities, and increase in non-face-to-face classes. More than half (52.8%) of college students have seen their physical activity decrease since the pandemic.¹ In college students, regular exercise can help reduce academic stress, fatigue, and body fat percentage (BFP), as well as improve physical strength, confidence, interpersonal networks, and weight management.¹⁷ Generally, human action is performed through two competing paths: intention and habit. For actions that are given prior thought and initiated with intention and cognitive effort, increasing action automaticity requires improving the strength of the habit related to the action, such that habit strength (after habit formation) eventually becomes stronger than the intention and cause of the behavior.¹⁸ Accordingly, this study implemented a 12-week walking program using the mobile app WalkON with college students and verified changes in their overall health status, lifestyle habits, mental health, social support, health-promoting behaviors, health beliefs, and self-efficacy levels after program completion.

Methods

Study design

This study adopted a quasi-experimental, non-equivalent control group, pre/post design. The independent variable was the 12-week WalkON-based walking exercise, and the dependent variables encompassed college students’ lifestyle habits (smoking, drinking, nutrition, and sleep), mental health indicators (depression and anxiety), social support (social networks), adherence to health-promoting behaviors, health beliefs, self-efficacy level, and anthropometric outcomes. WalkON serves as a contactless health management platform that promotes walking by analyzing individual physical activity data through smartphones, aiding in fostering healthy lifestyle habits.¹⁹ Despite the possibility of using this app in conjunction with wearable devices such as watches, this study was conducted using only smartphones.

For the experimental group, baseline pre-test anthropometric measurements, such as weight, BFP, skeletal muscle mass (SMM), and visceral fat level (VFL), were conducted and recorded, and participants completed questionnaires. They also engaged in the WalkON program over a 12-week period, during which they were instructed to walk for at least 30 minutes a day and a minimum of five times a week. Their continuous program engagement was bolstered through weekly step-count monitoring, the sharing of health-promotion educational materials on the app's bulletin board, and the organization of three incentivizing challenges. Following the program's conclusion, participants underwent a post-test encompassing the same questionnaire as that used in the pre-test and repeated the anthropometric measurements; they also completed a program satisfaction survey. The control group underwent the same anthropometric measurements and questionnaire administration at pre-test and after the 12-week period (post-test; Table 1), but did not engage in the program.

Table 1.

Overview of the study design.

Pre-test	Intervention (12 weeks)		Post-test
- General characteristics- Anthropometric variables(weight, BMI, SMM, and VFL)- Lifestyle habits(smoking, drinking, nutrition, and sleep)- Mental health(depression, anxiety, and social networks)- Health-promoting behaviors- Health beliefs- Self-efficacy	Experimental group	Control group	- Anthropometric variables(weight, BMI, SMM, and VFL)- Lifestyle habits(smoking, drinking, nutrition, and sleep)- Mental health(depression, anxiety, and social networks)- Health-promoting behaviors- Health beliefs- Self-efficacy- Program satisfaction(experimental group)

	Implementation of the WalkON program	None

Abbreviations: BMI: body mass index; BFP: body fat percentage; SMM: skeletal muscle mass; VFL: visceral fat level.

Participants

Utilizing the G*Power 3.1.7 program and considering a medium effect size of 0.6, a 95% significance level, and a power of 80%, the calculated minimum sample size was 40 participants for each group. In total, 102 participants were enrolled, and there were no dropouts.

Convenience sampling was employed. The experimental group was recruited through school bulletin boards and social network services (kakaotalk), with opportunities provided on a first-come, first-serve basis to students who could participate in the whole 12-week program (i.e. from September 25 to December 17, 2022). All participants were provided with information about the study process. Next, a control group was recruited using the same method, and students who agreed to participate and could participate in the questionnaire and take physical measurements before and after 12 weeks were enrolled.

Research tools

Lifestyle evaluation

Lifestyle habits were assessed using the lifestyle habit evaluation tools employed in the National Health Screening Program.¹⁹ Smoking status was measured using nine items on the amount and duration of past smoking, amount and duration of current smoking, intention to quit smoking, confidence in smoking cessation, and nicotine dependence. Alcohol consumption was gauged using a tool developed by the World Health Organization,²⁰ which comprises 10 items screening for high-risk drinking (three items), alcohol dependence (three items), and hazardous drinking (four items). Physical activity was assessed based on questions about the activities performed in the past 7 days.²¹ Nutrition-related items were derived from the Health Eating Index developed by Kim et al.²² and tailored to evaluate the nutritional quality of South Korean meals.

Sleep quality was primarily assessed using the Korean version of the Pittsburgh Sleep Quality Index,²³ which measures subjective sleep quality over the past month. This index comprises 19 questions grouped into seven subscales: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Each subscale is rated on a 4-point scale ranging from 0 to 3, the overall score of the scale ranges from 0 to 21,²³ higher scores indicate poorer sleep quality, and the cut-off for poor sleep quality is 5. The tool's reliability (i.e. Cronbach's α) was .83 at the time of development and .80 in this study, indicating good internal consistency.

Mental health and social support evaluation

Depression was assessed using the Korean version of the Patient Health Questionnaire (PHQ-9), originally developed by Kroenke et al.²⁴ and adapted and standardized into Korean by Donnelly.²⁵ The PHQ-9 assesses symptoms of depression experienced in the past 2 weeks and is suitable for simple depression screening and severity evaluation. The Cronbach's α of the scale was .89 at the time of development, .92 in Donnelly's study,²⁵ and .83 in this study, indicating good to excellent internal consistency.

Anxiety was assessed using the Korean version of the Beck Anxiety Inventory (BAI) developed by Beck et al. and adapted by Kwon.²⁶ The BAI comprises 21 items classified into cognitive, affective, and physical components of anxiety, and respondents report the level of anxiety experienced in the past week. The Cronbach's α of the scale was .91 in Kim and Yook²⁷ and .89 in this study, indicating excellent internal consistency.

Social support was assessed using the Lubben Social Network Scale,²⁸ which measures social bonds characterized by emotional, informational, material, and service exchanges through regular contact with family, relatives, neighbors, and friends. A higher score indicates a higher level of social support. The Cronbach's α of the scale was .78 in this study, indicating acceptable internal consistency.

Health-promoting behaviors

Health-promoting behaviors were assessed using the Health Promoting Lifestyle Profile II (HPLP II) developed by Walker et al.²⁹ and adapted and modified by Eom.³⁰ The HPLP II comprises 52 items classified into six subscales: physical activity (eight items), nutrition (nine items), spiritual growth (nine items), health responsibility (nine items), interpersonal relationships (nine items), and stress management (eight items). Each item is rated on a 4-point scale, with a higher score indicating a higher degree of engagement in health-promoting behaviors. The Cronbach's α of the scale was .94 at the time of development and .93 in this study, indicating excellent internal consistency.

Health beliefs

Health beliefs were assessed using Walker et al.'s HPLP II²⁹ as it was adapted into Korean by Lee and Jung.³¹ This 22-item health belief scale comprises four subscales: perceived susceptibility (four items), perceived severity (five items), perceived benefits (six items), and perceived barriers (seven items). The Cronbach's α of the scale was .92 at the time of development and .87 in this study, indicating good to excellent internal consistency.

Self-efficacy

Self-efficacy was assessed using the 17 items of the general self-efficacy subscale from the self-efficacy scale developed by Sherer et al.³² and later adapted by Kim et al.³³ A higher total score indicates higher self-efficacy expectations. The Cronbach's α of the scale was .90 in Kim et al.'s³³ study and .91 in this study, indicating excellent internal consistency.

Anthropometric variables

The anthropometric measurements of body weight, BFP, SMM, and VFL were measured using the InBody Dial product (InBodyDial H20N, Seoul, South Korea).

Data collection

Before commencing data collection, the study protocol was approved by the Institutional Review Board of Honam University (No. 1041223–202208-HR-10), Gwangju, South Korea. Participants were informed about study purposes, procedures, and ethical considerations, such as anonymity and confidentiality, before they provided their written informed consent. The study took place from September to December 2022. All participants successfully completed the pre-test, intervention (for the experimental group), and post-test, and had their data analyzed. Data on the number of steps in the experimental group were monitored every week, and the complete dataset for the study's duration was provided by Swallaby, the app's manager and owner, after this study was completed.

Data analysis

Data from health examinations and questionnaires were digitally recorded, and subsequent statistical analyses were conducted using SPSS version 25.0 (IBM). In addition to basic statistical methods to identify respondent characteristics, the analyses involved cross-tabulation analysis, paired t-tests, and unpaired t-tests.

Results

Participants’ general characteristics

The mean ages of the experimental and control groups were 20.6 and 20.3 years, respectively, with no significant age difference between groups (Table 2). Regarding sex, the experimental group comprised 50 female students (100.0%), while the control group comprised two men (3.8%) and 50 women (96.2%). All participants were college students. Most participants in the experimental group were sophomores (60.0%), followed by juniors (26.0%), freshmen (8.0%), and seniors (6.0%). In the control group, sophomores accounted for the largest proportion (44.2%), followed by freshmen (21.2%), juniors (19.2%), and seniors (15.4%). There was no significant difference in the distribution of academic years between the two groups (Table 2).

Table 2.

General and health-related characteristics of participants.

Variables	Exp (n = 50)	Cont (n = 52)	t or χ²	p
Variables	M ± SD or n (%)		t or χ²	p
Age (years)	20.56 ± 1.83	20.27 ± 1.37	0.294	.589
Sex
Male	0 (0.0)	2 (3.8)	1.962	.495
Female	50 (100.0)	50 (96.2)	1.962	.495
Academic years
Freshman	4 (8.0)	11 (21.2)	6.819	.077
Sophomore	30 (60.0)	23 (44.2)
Junior	13 (26.0)	10 (19.2)
Senior	3 (6.0)	8 (15.4)
Satisfaction with major
Very dissatisfied	0 (0.0)	1 (1.9)	1.686	.855
Dissatisfied	1 (2.0)	1 (1.9)
Neutral	12 (24.0)	12 (23.1)
Satisfied	25 (50.0)	29 (55.8)
Very satisfied	12 (24.0)	9 (17.3)
Living arrangements
Parental home	23 (46.0)	29 (55.8)	1.964	.637
Alone	10 (20.0)	10 (19.2)
Dormitory	16 (2.0)	13 (25.0)
At relatives	1 (2.0)	0 (0.0)
Other	0 (0.0)	0 (0.0)
Priority in college life
Academic achievements	34 (68.0)	29 (55.8)	3.102	.366
Interpersonal relationships	3 (6.0)	7 (13.5)
Career preparation	13 (26.0)	15 (28.8)
Dating	0 (0.0)	0 (0.0)
Others	0 (0.0)	0 (0.0)
Experience of pedometer use
Yes	27 (54.0)	20 (38.5)	2.477	.164
No	23 (46.0)	32 (61.5)	2.477	.164
Agreement with walking exercise
Strongly disagree	0 (0.0)	4 (7.7)	8.214	.081
Disagree	3 (6.0)	8 (15.4)
Neutral	19 (38.0)	18 (34.6)
Agree	19 (38.0)	18 (34.6)
Strongly agree	9 (18.0)	4 (7.7)
Height (cm)	162.01 ± 5.29	161.52 ± 4.96	0.116	.093
Weight (kg)	59.73 ± 12.01	56.72 ± 8.29	0.969	.200
BMI (kg/m²)	22.67 ± 3.99	21.71 ± 2.76	0.124	.052
BFP (%)	32.17 ± 6.15	32.50 ± 5.76	0.092	.200
SMM (kg)	21.58 ± 3.30	20.43 ± 3.25	0.086	.200
VFL	8.48 ± 4.10	8.21 ± 3.18	12.109	.726
Mobile app records
Total participation days	82.83
Total step count	508,041
Mean steps per day	6137

Abbreviations: BMI: body mass index; BFP: body fat percentage; SMM: skeletal muscle mass; VFL: visceral fat level; Exp: experimental group; Cont: control group.

As for satisfaction with college and major, both groups predominantly reported “neutral” or higher, with no significant group difference. Regarding current living arrangements, the experimental group had 23 participants living with family (46.0%), 16 living in dormitories (32.0%), 10 living alone (20.0%), and one living with relatives (2.0%). In the control group, most were living with family (55.8%), followed by living in dormitories (25.0%), and living alone (20.0%), with no significant group differences (Table 2). When asked about the most important aspect of college life, the majority in both groups ranked academic achievements as the top priority. In the experimental group, the priority order was academic achievements (68.0%), career preparation (26.0%), and interpersonal relationships (6.0%); the priority order was the same in the control group, but with different rates for academic achievements (55.8%), career preparation (28.8%), and interpersonal relationships (13.5%).

Regarding the experience of using a walking app, 27 participants (54.0%) had prior experience and 23 (46.0%) did not have such experience in the experimental group, and these figures were 20 (38.5%) and 32 (61.5%), respectively, in the control group, with no significant group difference (Table 2). Regarding agreement with walking exercises, most answered with neutral or above in the experimental group, with nine opting for strongly agree (18.0%), 19 agree (38.0%), 19 neutral (38.0%), and three disagree (6.0%). There was no significant difference between the two groups in this aspect (Table 2).

In the experimental group, the mean values for anthropometric measures were as follows: height at 162.01 cm, weight at 59.73 kg, body mass index (BMI) at 22.93 kg/m², BFP at 32.17%, SMM at 21.58 kg, and VFL at 8.48 (scale of 1–12). The control group had a mean height of 161.52 cm, weight of 56.72 kg, BMI of 22.35 kg/m², BFP of 32.50%, SMM of 20.43 kg, and VFL of 8.21. There were no significant group differences in these anthropometric and body composition measures.

In the experimental group, one participant (2.0%) currently smoked and consumed no more than 10 cigarettes daily. The control group mirrored this, with one individual (1.9%) smoking up to 10 cigarettes per day. Both smokers expressed intentions to quit within the upcoming 6 months. Regarding alcohol habits, three participants (6.0%) from each group reported drinking two to three times weekly, and when asked about challenges in moderating alcohol intake over the previous year, three respondents (6.0%) from each group admitted to facing difficulties approximately once a week. In the experimental group, participants used the WalkON program for an average of 82.83 days during the 12-week period, taking 508,041 steps (i.e. an average of 6137 steps per day).

Homogeneity test on dependent variables

As mentioned in the “Methods” section, before commencing the WalkON program, the baseline values for major variables in both groups were measured to assess homogeneity. Table 3 provides a detailed outline of pre-test results across various categories: lifestyle habits (nutrition and sleep), mental health (depression, anxiety, and social networks), and health-related psychosocial factors (health-promoting behaviors, health beliefs, and self-efficacy). No significant differences were observed between the two groups, and homogeneity was confirmed.

Table 3.

Results of homogeneity tests for major variables between the experimental and control groups.

Variables	Exp (n = 50)	Cont (n = 52)	t	p
Variables	M ± SD or n (%)		t	p
Nutrition	24.50 ± 3.20	23.98 ± 3.34	0.957	.060
Sleep	6.58 ± 2.78	5.54 ± 2.67	0.499	.482
Depression	3.28 ± 4.13	3.23 ± 3.25	0.663	.417
Anxiety	6.40 ± 9.01	5.42 ± 6.53	0.591	.444
Social network	26.90 ± 6.27	28.46 ± 6.13	0.966	.142
Health-promoting behaviors	2.29 ± 0.40	2.33 ± 0.39	0.984	.729
- Health responsibility	2.03 ± 0.48	2.04 ± 0.47	0.975	.350
- Physical activity	2.06 ± 0.64	2.02 ± 0.64	0.965	.132
- Nutrition	1.96 ± 0.51	2.04 ± 0.50	0.977	.416
- Interpersonal relationships	2.88 ± 0.53	2.92 ± 0.51	0.973	.274
- Spiritual growth	2.59 ± 0.64	2.65 ± 0.55	0.978	.452
- Stress management	2.19 ± 0.46	2.29 ± 0.51	0.952	.054
Health beliefs	3.16 ± 0.33	3.09 ± 0.40	0.962	.091
Self-efficacy	3.62 ± 0.73	3.61 ± 0.57	0.985	.741

Abbreviations: Exp: experimental group; Cont: control group.

Evaluation of the lifestyle-related effects of the WalkON program

The mean BMI increased from 22.67 kg/m² at pre-test to 22.74 kg/m² at post-test (p = .157) in the experimental group and from 21.71 to 21.84 kg/m² (p = .176) in the control group, with no significant group difference (p = .078). The mean BFP increased from 32.17% to 32.42% (p = .780) in the experimental group and from 32.50% to 32.89% (p = .691) in the control group, with no significant group difference (p = .199). The mean SMM decreased from 21.58 to 21.35 kg (p = .080) in the experimental group and increased from 20.43 to 20.54 kg (p = .225) in the control group, with no significant group difference (p = .440). The mean VFL (i.e. from 1 to 12) increased from 8.48 to 8.74 (p = .712) in the experimental group and from 8.21 to 8.31 (p = .553) in the control group, with no significant group difference (p = .103).

The nutritional status score in the experimental group decreased slightly from 24.50 (full score: 50) to 24.06 (p = .426), there was hardly any change in the control group (p = .874), and no significant group difference (p = .669). The sleep quality score dropped from 6.58 to 5.19 points (p = .990) in the experimental group and from 5.54 to 5.20 points in the control group (p = .058), and the difference between groups was significant (experimental group, decrease of 1.40 ± 4.04 points; control group, decrease of 0.34 ± 3.58 points; p = .027; Table 4).

Table 4.

Comparison of the anthropomorphic measurement- and lifestyle-related effects of the walkON program between the experimental and control groups.

Variables		Pre-test	Post-test	Unpaired t	p	Difference	Paired t	p
Variables		M ± SD		Unpaired t	p	M ± SD	Paired t	p
BMI (kg/m²)	Exp	22.67 ± 3.99	22.74 ± 3.82	1.427	.157	0.07 ± 0.63	1.781	.078
BMI (kg/m²)	Cont	21.71 ± 2.76	21.84 ± 2.81	1.363	.176	0.13 ± 0.54	1.781	.078
BFP (%)	Exp	32.17 ± 6.15	32.42 ± 6.08	0.279	.780	0.25 ± 2.48	1.294	.199
BFP (%)	Cont	32.50 ± 5.76	32.89 ± 5.94	0.398	.691	0.39 ± 2.52	1.294	.199
SMM (kg)	Exp	21.58 ± 3.30	21.35 ± 3.28	1.770	.080	−0.23 ± 0.79	0.769	.440
SMM (kg)	Cont	20.43 ± 3.25	20.54 ± 3.41	1.220	.225	0.10 ± 0.87	0.769	.440
VFL	Exp	8.48 ± 4.10	8.74 ± 4.01	0.370	.712	0.26 ± 0.82	1.643	.103
VFL	Cont	8.21 ± 3.18	8.31 ± 3.31	0.595	.553	0.09 ± 1.28	1.643	.103
Nutrition	Exp	24.50 ± 3.20	24.06 ± 3.10	0.800	.426	−0.44 ± 4.61	0.430	.669
Nutrition	Cont	23.98 ± 3.34	23.96 ± 3.15	0.159	.874	−0.19 ± 5.27	0.430	.669
Sleep	Exp	6.58 ± 2.78	5.19 ± 2.91	0.013	.990	−1.40 ± 3.58	2.244	.027*
Sleep	Cont	5.54 ± 2.67	5.20 ± 3.03	1.915	.058	−0.34 ± 4.04	2.244	.027*

Abbreviations: BMI: body mass index; BFP: body fat percentage; SMM: skeletal muscle mass; VFL: visceral fat level; Exp: experimental group; Cont: control group.

Evaluation of the mental health-related effects of the WalkON program

The mean depression score decreased from 3.28 to 2.96 (p = .947) in the experimental group and from 3.23 to 2.69 (p = .686) in the control group, with no significant group difference (p = .395).

The mean anxiety score decreased from 6.40 to 4.32 (p = .531) in the experimental group and from 5.42 to 3.67 (p = .617) in the control group, with a significant difference between groups (control group, decrease of 1.75 ± 7.82 points; experimental group, decrease of 2.08 ± 11.83 points; p = .020).

The social support score decreased from 26.90 to 26.62 (p = .207) in the experimental group and from 28.46 to 27.71 (p = .383) in the control group, with no significant group difference (p = .558; Table 5).

Table 5.

Comparison of the mental health-related effects of the WalkON program between the experimental and control groups.

Variables		Pre-test	Post-test	Unpaired t	p	Difference	Paired t	p
Variables		M ± SD		Unpaired t	p	M ± SD	Paired t	p
Depression	Exp	3.28 ± 4.13	2.96 ± 3.63	0.067	.947	−0.32 ± 5.82	0.854	.395
Depression	Cont	3.23 ± 3.25	2.69 ± 3.02	0.405	.686	−0.53 ± 4.34	0.854	.395
Anxiety	Exp	6.40 ± 9.01	4.32 ± 7.34	0.628	.531	−2.08 ± 11.83	1.941	.020*
Anxiety	Cont	5.42 ± 6.53	3.67 ± 5.60	0.501	.617	−1.75 ± 7.82	1.941	.020*
Social network	Exp	26.90 ± 6.27	26.62 ± 6.07	1.271	.207	−0.28 ± 8.72	0.588	.558
Social network	Cont	28.46 ± 6.13	27.71 ± 6.49	0.876	.383	−0.75 ± 9.18	0.588	.558

*p < .05.

Abbreviations: Exp: experimental group; Cont: control group.

Evaluation of the health-promoting behavior-related effects of the WalkON program

The mean health-promoting behavior score increased in both the experimental and control groups from 2.29 to 2.40 (p = .584) and from 2.33 to 2.36 (p = .650), respectively, with no significant group difference (p = .291).

Regarding changes by subscale, in the health responsibility subscale, the mean score increased from 2.03 to 2.28 (p = .903) in the experimental group and from 2.04 to 2.20 (p = .476) in the control group, with a significant group difference (experimental group, increase of 0.25 ± 0.71; control group, increase of 0.15 ± 0.73; p = .005).

In the physical activity subscale, the mean score increased from 1.96 to 2.00 (p = .748) in the experimental group and decreased from 2.02 to 1.94 (p = .691) in the control group, with no significant group difference (p = .438).

In the nutrition subscale, the mean score increased from 1.96 to 2.14 points (p = .389) in the experimental group and from 2.04 to 2.08 (p = .634) in the control group, with no significant group difference (p = .220).

In the interpersonal relationship subscale, the mean score increased from 2.88 to 2.97 (p = .664) in the experimental group and remained equal in the control group (2.92 in both pre- and post-test; p = .617), with no significant group difference (p = .614).

In the spiritual growth subscale, the mean score increased from 2.59 to 2.64 (p = .614) in the experimental group and decreased from 2.65 to 2.60 (p = .800) in the control group, with no significant group difference (p = 1.000).

In the stress management subscale, the mean score increased from 2.19 to 2.24 (p = .342) in the experimental group and from 2.29 to 2.36 (p = .816) in the control group, with no significant group difference (p = .111).

Regarding health beliefs, the mean score increased from 3.16 to 3.19 (p = .312) in the experimental group and from 3.09 to 3.18 (p = .908) in the control group, with no significant group difference (p = .256).

Regarding self-efficacy, the mean score increased from 3.62 to 3.97 (p = .957) in the experimental group and from 3.61 to 3.65 (p = .014) in the control group, and the difference between groups was significant (experimental group, increase of 0.34 ± 1.05; control group, increase of 0.03 ± 0.82; p = .046; Table 6).

Table 6.

Comparison of the health-promoting behavior-related effects of the WalkON program between the experimental and control groups.

Variables		Pre-test	Post-test	Unpaired t	p	Difference	Paired t	p
Variables		M ± SD		Unpaired t	p	M ± SD	Paired t	p
Health-promoting behavior	Exp	2.29 ± 0.40	2.40 ± 0.46	0.549	.584	0.11 ± 0.59	1.061	.291
Health-promoting behavior	Cont	2.33 ± 0.39	2.36 ± 0.47	0.456	.650	0.02 ± 0.66	1.061	.291
- Health responsibility	Exp	2.03 ± 0.48	2.28 ± 0.59	0.122	.903	0.25 ± 0.71	2.875	.005**
- Health responsibility	Cont	2.04 ± 0.47	2.20 ± 0.59	0.716	.476	0.15 ± 0.73	2.875	.005**
- Physical activity	Exp	2.06 ± 0.64	2.00 ± 0.61	0.322	.748	−0.06 ± 0.78	0.778	.438
- Physical activity	Cont	2.02 ± 0.64	1.94 ± 0.72	0.398	.691	−0.07 ± 1.10	0.778	.438
- Nutrition	Exp	1.96 ± 0.51	2.14 ± 0.59	0.865	.389	0.18 ± 0.90	1.233	.220
- Nutrition	Cont	2.04 ± 0.50	2.08 ± 0.55	0.478	.634	0.03 ± 0.86	1.233	.220
- Interpersonal relationships	Exp	2.88 ± 0.53	2.97 ± 0.53	0.436	.664	0.08 ± 0.79	0.506	.614
- Interpersonal relationships	Cont	2.92 ± 0.51	2.92 ± 0.52	0.501	.617	−0.00 ± 0.77	0.506	.614
- Spiritual growth	Exp	2.59 ± 0.64	2.64 ± 0.64	0.506	.614	0.04 ± 0.89	0.000	1.000
- Spiritual growth	Cont	2.65 ± 0.55	2.60 ± 0.58	0.254	.800	−0.04 ± 0.85	0.000	1.000
- Stress management	Exp	2.19 ± 0.46	2.24 ± 0.53	0.954	.342	0.14 ± 0.65	1.606	.111
- Stress management	Cont	2.29 ± 0.51	2.36 ± 0.55	0.234	.816	0.07 ± 0.73	1.606	.111
Health belief	Exp	3.16 ± 0.33	3.19 ± 0.39	1.017	.312	0.02 ± 0.53	1.143	.256
Health belief	Cont	3.09 ± 0.40	3.18 ± 0.33	0.116	.908	0.09 ± 0.56	1.143	.256
Self-efficacy	Exp	3.62 ± 0.73	3.97 ± 0.72	0.054	.957	0.34 ± 1.05	2.019	.046*
Self-efficacy	Cont	3.61 ± 0.57	3.65 ± 0.53	2.490	.014	0.03 ± 0.82	2.019	.046*

*p < .05. **p < .01.

Abbreviations: Exp: experimental group; Cont: control group.

Results of the program satisfaction survey

On the program satisfaction survey, results showed a mean score of 4.60 ± 0.67 (total score of 5) for the statement “Participating in the program was a valuable experience,” 4.42 ± 0.75 for “I was able to adopt health-promoting behaviors through the program,” 4.30 ± 0.79 for “I felt a sense of achievement during the program participation,” and 4.42 ± 0.64 for “I enjoyed walking tracked by the mobile app.”

Discussion

This study implemented the WalkON program, a walking regimen tracked by the mobile app WalkON, among college students to identify strategies that could help enhance students’ health behaviors. Over a 12-week period, college students partook in the WalkON program for an average of 82.83 days, accumulating 508,041 steps, which translates to an average of 6137 steps per day. Looking at students’ walking exercise engagement during the program, they mainly walked a lot during the day when they were going to school and leaving school, and there was no difference by weekday. During the entire period, there was a slight decrease in walking exercise during the midterm exam period and a decrease toward the end of the program. These two periods related to the beginning of the final exams, of winter, and of a colder weather. According to the results of a report using randomly extracted sample data of 22,861 Seoul citizens for 1 year in 2020 and prepared by the research team that developed the WalkON app, the average walking practice rate (i.e. at least 30 minutes per session, a minimum of five times a week) was 52.3% and the average number of steps was 4,898³⁴; a comparison with the numbers observed in this study showcases that the average number of steps for college students was somewhat high in relation to the general population.

The sleep quality of the experimental group significantly improved compared to that of the control group after the program. This enhancement could be attributed to the potential positive impacts of regular walking exercises on sleep quality. In the pre-test, the mean sleep quality scores for the experimental and control groups stood at 6.58 and 5.54, respectively, reducing to 5.19 and 5.20, respectively, at post-test, and showcasing a grander improvement in sleep quality in the experimental group and general improvements in both groups. A prior study on the relationship between walking exercise and sleep confirmed the positive effect of walking on sleep in women aged 55 years or older who were receiving hormone therapy for breast cancer, with a notable influence on serotonin levels.³⁵ Another study exploring the correlation between walking activities and sleep quality in community-dwelling older adults revealed that both total weekly walking time and daily walking frequency were crucial predictors of sleep quality; specifically, walking more than once daily and accumulating a total walking time exceeding 210 minutes weekly was associated with better sleep quality.³⁶

The anxiety level in the experimental group significantly decreased after the program's implementation compared to that of the control group. This aligns with research findings that middle-aged women who walk more than three times a week experience a positive impact on anxiety compared to those who do not exercise.³⁷ Moreover, a study based on data from 1963 participants and recorded on a website using pedometer or other fitness monitor readings through the Corporate Stepathlon Challenge (http://www.stepathlon.com) showed that a 100-day challenge improved levels of anxiety, depression, and stress.³⁸ A separate study involving 506 young individuals during the COVID-19 pandemic found a significant correlation between anxiety levels and physical activity.³⁹ During the pandemic, young adults notably reduced their physical activity, spent more time in sedentary behaviors, and walked considerably less. A prior qualitative study conducted with South Korean college students showed that they reported experiencing negative emotions such as anxiety, frustration, regret, resignation, and giving up owing to the expansion of social distancing and continued remote education measures related to the pandemic.⁴⁰ Since students who participated consistently practiced walking for 12 weeks and showed reduced anxiety, the program could have led to a reduction in their anxiety levels.

The level of self-efficacy in the experimental group increased significantly after the intervention compared to that of the control group. This observation aligns with the findings of other studies that have reported enhanced self-efficacy owing to regular walking.^41,42 A study involving a 10-week walking intervention with office workers also showed a significant increase in self-efficacy.⁴¹ Furthermore, after a 24-week walking regimen, patients with ischemic heart disease showcased heightened self-efficacy and improved physical activity levels.⁴² In this study, college students in the experimental group engaged consistently in walking exercises for 12 weeks and experienced accomplishments, which likely augmented their confidence and bolstered their self-efficacy. There was also a plethora of information posted on the mobile app WalkON's bulletin board and educational materials about diet and nutrition and walking videos, and participants engaged in incentivizing walking challenges. The combination of these different contents in the program could have instilled confidence in college students to meet their personal exercise objectives and might have fostered an enhancement in self-efficacy driven by emotional engagement and behavioral shifts.

After the intervention, anthropometric variables such as BMI, weight, BFP, SMM, and VFL did not exhibit significant changes. This contrasts with findings from both domestic and international studies, which showed that increased walking led to decreased body fat levels and vice versa,^43,44 and reduced cardiovascular disease risk factors among obese participants.⁴⁵ However, the current results were consistent with those of a study in which no significant differences were observed in weight and BMI in college students after an 8-week walking regimen;⁴⁶ they also comply with findings from a research involving obese dormitory-resident college students who underwent a 10,000-step walking exercise for 12 weeks, which resulted in non-significant reductions in weight, BMI, and body fat.⁴⁷ It is important to emphasize that the methodology of this study differs somewhat from that of these studies focused on obese groups, and hence major physical changes might not have been evident in this study. Further, the program began in late September, after the academic semester began, and ran for 12 weeks to end around the final exam period; the specific period during which this study was conducted allows for inferring that factors such as temperature changes and exam schedules might have influenced students’ compliance with walking, which may have ultimately influenced weight and body fat. This can be verified by the average number of steps taken by the experimental group by month, as follows: 6736 in September, 6146 in October, 6522 in November, and 5143 in December.

Depression levels decreased in both the experimental and control groups with no significant group difference. However, it has been confirmed that physical activities, such as walking, reduce the experience of depression and lower the risk of depression onset.⁴⁸ In a Norwegian cohort study of 33,908 healthy adults who were followed over an 11-year observation period, after adjusting for factors such as physical illnesses, changes in autonomic nervous system activation, and low social support levels, exercise was estimated to reduce the future onset of depression by 12%.⁴⁹ The non-significant changes in this study could be attributable to participants being generally healthy or not having any pre-existing mental health conditions and psychological phenomena such as depression can also be easily influenced by various external factors.

Generally, when considering the different findings across the variables analyzed, the use of a mobile app in the program was considered an efficient strategy. Specifically, the app allowed participants to self-monitor by visualizing steps and calories burned, fostered a community in which participants could share step counts, and was carefully designed to promote sustained physical activity through ongoing motivational activities (e.g. weekly step monitoring, sharing health improvement materials on the app's bulletin board, and initiating three walking challenges). Consistently, the evolution of information technology has paved the way for health management services to actively utilize smartphone apps and social networks as effective tools in physical activity interventions.⁵⁰

This study had two key limitations. First, there was a lack of recruitment randomization: the sample comprised only college students from one region. In addition, owing to the nature of the exercise intervention, blinding could not be performed, which limited the verification of the effectiveness of the program. Generalizations should thus be made with caution. Second, the walking exercise program was conducted without controlling for individual stresses and dietary habits, and physiological indicators were not utilized.

Future studies are suggested to verify changes in physiological indicators through mobile app usage and assess the benefits of in-app community operations, as well as the impact of communication media such as social networking sites and telephone counseling. Moreover, to enhance the usefulness of measurements, experiments involving walking programs that employ wearable devices should be conducted to test their efficacy. These future studies will aid in developing a diverse array of intervention strategies to encourage physical activities among college students.

Conclusions

After implementing a 12-week walking program for college students using the mobile app WalkON, positive effects were observed in reducing anxiety and enhancing sleep quality, health responsibility, and self-efficacy. This study is significant in that it delivers evidence of a program using mobile devices (i.e. remote environment) that could be useful in encouraging college students to increase their regular physical activity through walking exercises in the post-COVID-19 period. The 12-week program, encompassing three 4-week challenge activities, daily monitoring of step counts and activity duration, and the provision of valuable health information (e.g. exercise tips, diet, and nutrition) via the in-app bulletin board, was generally demonstrated to be effective in its purposes.

This study also showed that some college students may need and want to engage in exercise. Encouraged by the positive outcomes from the intervention, it is hoped that health-promotion programs, which can be relatively easily integrated into people's daily routines, will gain momentum and lead to the further enrichment of evidence on the topic.

Footnotes

Acknowledgments

I thank the participating students. I also thank Editage (

) for English language editing and journal submission support. I have authorized the submission of this manuscript through Editage.

Contributorship

The author was responsible for all aspects of this study.

Declaration of conflicting interests

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

Ethical approval

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Honam University (No. 1041223–202208-HR-10).

Funding

This study was supported by a research fund from Honam University,2022.

Guarantor

None.

ORCID iD

Yun-Su Kim

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Effects of a walking program using the “WalkON” mobile app among college students

Abstract

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods

Study design

Participants

Research tools

Lifestyle evaluation

Mental health and social support evaluation

Health-promoting behaviors

Health beliefs

Self-efficacy

Anthropometric variables

Data collection

Data analysis

Results

Participants’ general characteristics

Homogeneity test on dependent variables

Evaluation of the lifestyle-related effects of the WalkON program

Evaluation of the mental health-related effects of the WalkON program

Evaluation of the health-promoting behavior-related effects of the WalkON program

Results of the program satisfaction survey

Discussion

Conclusions

Footnotes

Acknowledgments

Contributorship

Declaration of conflicting interests

Ethical approval

Funding

Guarantor

ORCID iD

References