Association of Lifestyle Type with Mental Health and Quality of Life in Community-Dwelling Middle-Aged and Older Adults: A Latent Profile Analysis

Abstract

Identify the values reflected in the healthy lifestyle of middle-aged and older Adults people and to confirm their association with mental health, quality of sleep, and quality of life. Data were collected from 200 middle-aged and older Adults and descriptive statistics and latent profile analyses were performed. One-way ANOVA and multinomial logit regression were performed on the differences between latent classes and predictors. Classes for latent-class profile were as follows: Class 1—Basic lifestyle and balanced pursuit; Class 2—Imbalanced lifestyle and selective pursuit; Class 3—Inactive lifestyle and indifferent; and Class 4—Active lifestyle and active pursuit. Difference in the according to latent classes, Class 2 showed the highest depression and quality of sleep problems and the lowest quality of life. It was confirmed that the higher the latent-class opinion and interest in activities and health, the higher the quality of life. Considering the diversity of this latent-class lifestyle, it will be possible to explore strategies for health promotion.

Keywords

middle-aged and older Adults latent profile analysis healthy lifestyles values

Introduction

In the aging process, increasing age is acting as a risk factor for diseases (Niccoli & Partridge, 2012). In particular, the deterioration of health conditions due to disease is reported as a serious problem among middle-aged and older Adults people, and the need for interventions is being emphasized (McGrath et al., 2019). Academic research to lead older Adults people to successful aging is common, but there is a view that middle age, which is in the continuum of human development, should be expanded and applied (Calasanti, 2016).

The factors that determine successful aging are unclear, but lifestyle factors such as smoking, physical activity, and nutritional habits that can be controlled by individuals are being emphasized as important factors (Franklin & Tate, 2009). Partridge et al. (2018) reported that the lifestyle and activities of an individual can help lower the risk of several health problems that can appear with age and maintain a healthy lifespan. In addition, improving and maintaining a lifestyle that can be individually controlled in a healthy manner is essential to ensure good health and well-being (Haapasalo et al., 2018).

Lifestyles that affect health and well-being are behaviors revealed in daily life due to factors such as physical activity, dietary habits, smoking, and drinking (World Health Organization, 2021). Such a lifestyle measures and analyzes the exposure and frequency of related risk factors and behaviors in a quantitative way (Ferreira et al., 2018). However, since lifestyle is not only expressed in simple actions in everyday life, but also can have diversity according to individual beliefs, values, and opinions, the approach to this should be considered together (Anderson & Golden, 1984).

COVID-19, which has been prevalent since 2019, has affected people’s lifestyles as lockdowns, social and physical distancing, and mandatory quarantine measures have been taken in certain countries and regions. As people spend more time at home, they experience unhealthy lifestyle changes such as social isolation, reduced physical activity, sedentary behavior, eating behavior, and sleep pattern (Rawat et al., 2021). These lifestyle changes have negatively impacted individuals’ health, well-being, and quality of life (Diedhiou et al., 2021). In particular, middle-aged and older Adults people who are more vulnerable to infectious diseases than other age groups showed a further deteriorating lifestyle (Machón Sobrado et al., 2021), and it was reported that they had a fear of infection (De Leo & Trabucchi, 2020).

As an approach to understanding a diverse lifestyle, the macro-level approach has limitations in that it cannot understand the context in which individual behavior is induced because it deals with the trends of society as a whole or its members (Oyserman et al., 2014). In contrast, the micro-level approach focuses on the demographic, socioeconomic, and psychographic attributes of individuals, with an emphasis on choices and decisions about the behaviors expressed (Thomas, 2022). According to Vuong et al. (2022), the importance is emphasized because the mindset based on the values system at the microscopic level selects and determines individual behavior. Considering the importance of lifestyle based on the value system and the need for research, it will be useful to improve and promote the health of social groups through an understanding of lifestyles via a micro-level approach to individual daily activities, interests, and opinions (Cepni et al., 2021).

In this study, we tried to understand the health lifestyle of the middle-aged in a microscopic approach based on values through the activity, interest, and opinion (AIO) (Plummer, 1974) analysis method. A potential profile for the values reflected in the healthy lifestyle was classified, and the differences in mental health, quality of sleep, and quality of life were verified according to each potential profile. The purpose of this study is to promote healthy aging by providing basic data on the values that the healthy lifestyle of the older Adults reflects and expresses.

Methods

Study Design and Participants

A cross-sectional survey was conducted for the purpose of this study. An online survey was administered owing to social distancing restrictions introduced to prevent the spread of COVID-19. The survey data were collected through a specialized online research company (www.embrain.com, accessed July 13, 2022). The company has a survey panel database of about 1,589,236 members and manages the survey quality through new member verification and the existing panel rating system. The survey was sent by e-mail about the research participation information to the target group that met the selection criteria of this study, and when responding to the survey, the survey was conducted through the URL of the company server. To help understand the contents of the survey, the purpose and method of the survey were also presented.

The selection criteria for this study are as follows: (1) aged 55 or older living in the community, (2) ability to communicate fluently, (3) comfortable reading in Korean, and (4) voluntarily agreed to participate in the study. The survey period was from April 4 to 14, 2022 (10 days). About 367 people participated in this survey, and the final data of 200 people were secured through the data verification stage of the company. Ethical approval of this study was obtained from the Institutional Review Board of Yonsei University (approval number: 1041849-202207-SB-135-03).

Measures

Yonsei Lifestyle Profile-Value (YLP-V)

Lifestyle Profile-Value (YLP-V) was used to measure the value system reflected in the lifestyle of the older Adults (Y. M. Lim & Park, 2022). YLP-V was developed based on Plummer’s (1974) AIO (Activities, Interests, Opinions) technique, which enables profiling at the microscopic level, and consists of 24 sub-concepts according to a three-dimensional view. It enables an understanding of the lifestyle of the older Adults based on individual values and identifies the characteristics that distinguish them from others. Respondents indicated how often they experienced each symptom on a 5-point Likert scale consisting of “strongly disagree,”“disagree,”“neither agree nor disagree,”“agree,” and “strongly agree.” It was developed using the AIO and Delphi methods. The average content validity ratio of the final Delphi survey was .89, stability was .16, convergence was .42, and the consensus was .76, which is high (Y. M. Lim & Park, 2022).

Self-Rating Anxiety Scale (SAS)

The Zung SAS is a 20-item measurement method developed to evaluate the frequency of anxiety symptoms based on diagnostic conceptualization (Zung, 1971). The scale focuses on physical symptoms. Respondents indicated how often they experienced each symptom on a 4-point Likert scale consisting of “a little of the time,”“some of the time,”“a good part of the time,” and “most of the time.” Items 5, 9, 13, 17, and 19 were inverse scores, and the total score for SAS ranged from 0 to 80. These include internal consistency (Cronbach’s alpha = .82) (Tanaka-Matsumi & Kameoka, 1986); concurrent validity (r = .30, Taylor Manifest Anxiety Scale) (Zung, 1971).

Patient Health Questionnaire-9 (PHQ-9)

The PHQ-9 is a 9-item self-report measure of depression consisting of nine items matching the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for major depression (Manea et al., 2015). Respondents indicate how severely they experienced each item on a 4-point Likert Scale consisting of “not at all,”“several days,”“more than half the days,” and “nearly every day.” The total score for the PHQ-9 ranges from 0 to 27, with high scores indicating high levels of depression.

Insomnia Severity Index (ISI)

The ISI is a 7-item self-report questionnaire that assesses the nature, severity, and impact of insomnia during the preceding 2 weeks. Its seven items address (1) difficulty falling asleep, (2) difficulty staying asleep, (3) problems waking too early, (4) sleep dissatisfaction, (5) interference of sleep problems with daytime functioning, (6) noticeability of sleep difficulties to others, and (7) the level of distress caused by sleep difficulties (Jun et al., 2022). It measures these items on a 5-point Likert scale ranging between “no problem” and “very severe problem.” The total scores range from 0 to 28, with higher scores indicating greater insomnia severity (Morin et al., 2011).

Fear of COVID-19 (FCV-19S)

The FCV-19S is an evaluation tool that measures the degree of fear of COVID-19 (Ahorsu et al., 2020). It consists of seven items and uses a 5-point Likert scale ranging from one point (strongly opposed) to five points (strongly in favor). The total score is a possible total score range from 7 to 35, calculated by summing all item scores (Alyami et al., 2021). The higher the score, the greater the fear of contracting COVID-19. The scale showed high internal consistency (α = .82) (Ahorsu et al., 2020).

World Health Organization Quality of Life-Brief (WHOQOL-BREF)

The WHOQOL-BREF evaluates the quality of life in terms of individuals’ perceptions of their quality of life over the past 2 weeks using a 26-item self-report questionnaire. It consists of physical health, psychological health, social relationships, the environment, and overall sub-areas (WHOQOL Group, 1995). It is composed of a 5-point Likert scale, and the higher the score, the higher the quality of life (Aigner et al., 2006). The WHOQOL-BREF domain scores showed good discriminant validity (physical health, psychological health, social relationships, and environment), content validity, and internal consistency (Cronbach’s alpha: physical health, .80; psychological health, .76; social relationships, .66; and environment, .80), and test–retest reliability (WHOQOL Group, 1998).

Data Analysis

Frequency and descriptive statistical analyses were conducted to determine the general characteristics of the study participants. Descriptive statistical analyses were performed using SPSS version 22 (IBM, Armonk, NY, USA). A latent profile analysis (LPA) was conducted to identify and explain lifestyle habits that reflect the values of middle-aged and older Adults people living in the community. LPA is a type of mixture modeling that classifies individuals into subgroups based on the heterogeneity of the data (Muthén, 2002; Schmiege et al., 2012). LPA was analyzed using Mplus 8.4 (Muthén and Muthén, Los Angeles, CA, USA). For LPA, activities, interests, and opinions on the lifestyles of middle-aged and older Adults people were used. In addition, the data used in this study were processed using the full information maximum likelihood method for processing missing values as missing data (Lee, 2014).

In general, the final model is estimated by analyzing the suitability of each model while gradually increasing the number of groups from one group on the premise of the interdependence of the indicators observed in the LPA (M. R. Lim, 2014). The number of latent classes in the final model is determined using the goodness-of-fit index, the significance comparison between k and k−1 groups, and the entropy index. In general, the goodness-of-fit indices used for latent groups are Akaike Information Criteria (AIC; Akaike, 1987), Baysian Information Criteria (BIC; Schwarz, 1978), and sample-size adjusted BIC (saBIC; Sclove, 1987), and the lower each value, the better the fit of the model. In addition, the entropy index is used to evaluate the accuracy of the classification and appears in the range of 0 to 1. The closer the value is to 1, the more accurately classified the observed object from a specific group (Jedidi et al., 1997). The Lo-Mendell-Rubin-adjusted likelihood ratio test (LMR-LRT) was used as a statistical significance test to compare the k- and k−1 groups of latent profile analyses, providing a significance level to determine whether each model is supported (Lo et al., 2001).

Results

Characteristics of the Study Sample

The majority of community-dwelling older adults included in this study were aged 55 to 64 (75%). The majority had graduated from college or university (67.5%) and lived in metropolitan areas (61.5%), as presented in Table 1, which summarizes their sociodemographic characteristics.

Table 1.

Sociodemographic Characteristics (N = 200).

Variables		N	(%)
Sex	Male	100	50
Sex	Female	100	50
Age (Mean ± SD: 60.2 ± 4.3)	55–64	150	75
	65–74	44	22
	>75	3	3
Educational attainment	Elementary school	2	1
	Middle school	4	2
	High school	59	29.5
	College or university	135	67.5
Residential area	Metropolitan	123	61.5
	Provincial big city	42	21
	Small and medium-sized cities	32	16
	Countryside	3	1.5
Regular medication intake	Yes	132	66
Regular medication intake	No	68	34

Classification of Values Reflected in the Lifestyle

The LPA fit indices for the two to five classes are shown in Table 2. The four-class model was determined to be the best-fitting model. Although the AIC, BIC, and adjusted BIC decreased slightly from four to five classes, the LMRT suggests that the four classes are optimal. The entropy for the four classes was 0.924, which allowed better distinction between the profiles. Therefore, the four-class model represents four meaningful subpopulations of middle-aged and older Adults individuals, based on their lifestyle type. Figure 1 shows the standardized mean (Z-score) for each lifestyle value.

Table 2.

Identification of the Number of Profiles.

No. of class	AIC	BIC	SS-ABIC	LMR-LRT	Entropy	Latent profile proportions (%)
No. of class	AIC	BIC	SS-ABIC	LMR-LRT	Entropy	1	2	3	4	5
2	4,525.025	4,627.273	4,529.061	452.517***	0.872	40.1	59.9
3	4,412.458	4,550.987	4,417.927	132.297	0.885	32.6	53.6	13.8
4^a	4,277.211	4,452.021	4,284.112	154.595**	0.924	51.1	9.9	25.4	13.6
5	3,951.356	4,162.449	3,959.690	82.369	0.940	15.5	4.0	23.5	18.0	39.0

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion; SS-ABIC = sample-sized adjusted Bayesian information criterion; LMR-LRT = Lo-Mendell-Rubin adjusted likelihood ratio test.

^aThe class model is the best-fitting model.

p < .005. ***p < .001.

Figure 1.

Standardized mean for each lifestyle values by latent class.

Class 1

Class 1 was labeled “basic lifestyle and balanced pursuit type,” characterized by participation in productive activity or rest, interest in health-related education, foods, and habits. Class 1 was the largest, accounting for 51.1% of the sample.

Class 2

Class 2 was characterized by a low level of participation in activities, including social and physical activity, and a high level of interest in health, which accounted for 9.9% of the sample and was named the “imbalanced lifestyle and selective pursuit type.”

Class 3

Class 3 (25.4%) was named the “inactive lifestyle and indifferent type,” characterized by a low level of participation in activities and interest regarding health-related issues.

Class 4

In contrast, Class 4 was characterized by a high level of participation in meaningful activities and interest in health and was named the “active lifestyle and active pursuit type.”Figure 2 shows the trends of the four lifestyle types.

Figure 2.

Tendencies of the four distinctive lifestyle types.

Characteristics of the Identified Profiles

The demographic characteristics of the four classes are shown in Table 3. There were no significant differences in demographic characteristics among these classes. However, there were statistically significant differences in the levels of depression, quality of sleep, and quality of life among the four classes (Table 4). Class 2, which is an imbalanced lifestyle and selective pursuit type, demonstrated the highest level of depression, sleep issues, and low quality of life compared to other classes.

Table 3.

Demographic Characteristics of Identified Four Classes.

		Latent class: N (%)				χ²
Variables		Class 1	Class 2	Class 3	Class 4	χ²
Sex	Male	51 (49.5)	11 (57.9)	27 (52.9)	11 (40.7)	1.586
Sex	Female	52 (50.5)	8 (42.1)	24 (47.1)	16 (59.3)	1.586
Age	55–64	71 (68.9)	17 (89.5)	43 (84.3)	19 (70.4)	8.717
	65–74	27 (26.2)	2 (20.5)	7 (13.7)	8 (29.6)
	>75	5 (4.9)	—	1 (2.0)	—
Educational attainment	Elementary school	1 (1.0)	—	1 (2.0)	—	15.404
	Middle school	4 (3.9)	—	0	—
	High school	31 (30.1)	9 (47.4)	16 (31.4)	3 (11.1)
	College or university	67 (65.0)	10 (52.6)	34 (66.7)	24 (88.9)
Residential area	Metropolitan	60 (58.3)	12 (63.2)	34 (66.7)	17 (63.0)	4.148
	Provincial big city	23 (22.3)	3 (15.8)	10 (19.6)	6 (22.2)
	Medium-sized cities	17 (16.5)	4 (21.1)	7 (13.7)	4 (14.8)
	Countryside	3 (2.9)	—	—	—
Regular medication intake	Yes	71 (68.9)	13 (68.4)	33 (64.7)	15 (55.6)	1.795
Regular medication intake	No	32 (31.1)	6 (31.6)	18 (35.3)	12 (44.4)	1.795

Note. Class 1: basic lifestyle and balanced pursuit type; Class 2: imbalanced lifestyle and selective pursuit type; Class 3: inactive lifestyle and indifferent type; and Class 4: active lifestyle and active pursuit type.

p < .05. **p < .01. ***p < .001.

Table 4.

Comparison of Classes Based on Mental Health and Quality of Life.

Variables	Latent class: M (SD)				F
Variables	Class 1	Class 2	Class 3	Class 4	F
Anxiety (SAS)	36.22 (5.04)	36.95 (4.85)	35.80 (6.00)	36.63 (6.87)	0.252
Depression (PHQ-9)	12.38 (3.70)	15.63 (3.74)	14.33 (4.49)	12.15 (4.20)	5.805***
Quality of sleep (ISI)	14.31 (5.86)	20.11 (6.57)	16.65 (5.84)	14.89 (7.41)	5.514***
Fear of COVID-19	17.73 (5.77)	18.05 (6.96)	17.82 (6.13)	15.93 (5.74)	0.775
Quality of life (WHOQOL-BREF)	87.64 (11.83)	70.26 (13.16)	76.49 (12.06)	93.25 (13.75)	22.384***

Note. Class 1: basic lifestyle and balanced pursuit type; Class 2: imbalanced lifestyle and selective pursuit type; Class 3: inactive lifestyle and indifferent type; and Class 4: active lifestyle and active pursuit type. SAS = Self-Rating Anxiety Scale; PHQ-9 = Patient Health Questionnaire-9; ISI = Insomnia Severity Index.

p < .05. **p < .01. ***p < .001.

Association Between Value of Lifestyle Profiles and Mental Health

For depression, quality of sleep, and quality of life, Table 5 indicates that a significant association was found between classes 1, 2, and 3, compared to Class 4. For example, the community-dwelling middle-aged and older Adults in Class 1, “basic lifestyle and balanced pursuit type,” were more likely than those in Class 4 to have a lower quality of life. The same trend was observed for adolescents in Classes 2 and 3 compared to Class 4.

Table 5.

Association Between Mental Health and Classes.

		β	SE	OR	95% CI
Class 4 versus
Class 1	Depression (PHQ-9)	−.068	0.093	0.533	[0.779, 1.121]
	Quality of sleep (ISI)	−.059	0.048	1.525	[0.859, 1.035]
	Quality of life (WHOQOL-BREF)	−.070**	0.026	7.003	[0.886, 0.982]
Class 2	Depression (PHQ-9)	−.226	0.129	3.069	[0.620, 1.027]
	Quality of sleep (ISI)	.035	0.068	0.262	[0.906, 1.184]
	Quality of life (WHOQOL-BREF)	−.193***	0.039	24.375	[0.764, 0.890]
Class 3	Depression (PHQ-9)	−.127	0.105	1.459	[0.718, 1.082]
	Quality of sleep (ISI)	−.056	0.056	1.006	[0.847, 1.055]
	Quality of life (WHOQOL-BREF)	−.152***	0.031	23.627	[0.807, 0.913]

p < .05. **p < .01. ***p < .001.

Discussion

We measured and analyzed the lifestyles that reflect each individual’s diverse values. This is important in terms of enabling an in-depth understanding of the characteristics, behaviors, and needs of middle-aged and older Adults people. This study attempted to identify lifestyles reflecting the values of middle-aged and older Adults individuals through LPA, and to confirm the association of variables with mental health, quality of sleep, and quality of life.

Latent-class profiles for lifestyle reflecting the values of middle-aged and older Adults comprehensively considered the fit indices, entropy, and the proportions of the latent -lass through LPA. When the latent-class profiles were divided into four classes, it was determined as the optimal model, and the proportions of all classes were more than 5%, which was judged to represent the latent-class characteristics (Merz & Roesch, 2011). Each latent class was named in consideration of diversity and participation in daily activities, interests, and opinions according to the characteristics of the YLP-V on lifestyle.

In the latent-class for lifestyle, “basic lifestyle and balanced pursuit type” appeared evenly in all activities, interests, and opinions, and the largest number of samples was included in the entire sample (Class 1, 51.1%). It was found that the “imbalanced lifestyle and selective pursuit type” preferred relaxation activities and emphasized the importance of a healthy lifestyle, although there was low interest in health (Class 2, 9.9%). The “inactive lifestyle and indifferent type” were inactive in all activities, and both interest and opinions on a healthy lifestyle were low (Class 3, 25.4%). The “active lifestyle and active pursuit type” emphasized active participation and high interest and opinions in all activities (Class 4, 13.6%). In previous studies, it was discussed that it would be possible to contribute to the promotion of public health by discussing the risks of health conditions, focusing on healthy or unhealthy behaviors (Grant et al., 2019). However, we determined that empirical plans and practices for health promotion would become possible when we understand the context that triggers individual behavior. Until now, no research has attempted to analyze lifestyle reflecting individual values by latent-class profiles. Analyzing the characteristics by classifying types according to the needs and demands for a healthy lifestyle of the middle-aged and older Adults will enable effective health promotion and communication with the public (Hornik, 1989). Therefore, a healthy lifestyle latent class based on values is considered an attempt to explore strategies to improve the health of people constituting a social group.

The differences between the four lifestyle classes were confirmed. The lifestyle latent class did not show any significant differences according to the demographic characteristics of gender, age, educational background, residential area, and drug use. However, it was confirmed that latent Class 2 had higher levels of depression and problems with sleep quality compared to the other latent classes, and a lower quality of life. Latent-class 2 showed imbalanced characteristics due to low participation in all activities except relaxation activities. This finding supports the results of previous studies reporting that low activity levels can increase depression (Schuch et al., 2018) and sleep problems (Wang & Boros, 2021). In addition, participation in meaningful activities has been reported to have a positive effect on the quality of life (Owen et al., 2021). With regard to the relationship between participation in activities and quality of life, depression and sleep quality were found to have a causal relationship as a parameter (Bae et al., 2012; Yuan et al., 2020). Therefore, considering the possibility of problems with mental health, quality of sleep, and quality of life in people with low activity participation levels, education, and interventions that emphasize activity participation are necessary. With increasing discussion on participation in activities as an important health factor influencing lifestyles, it is necessary to focus on it (Carl et al., 2020).

The association between predictive factors was analyzed by setting the analyzed latent class as a comparative class. As a result of the analysis, latent classes 1, 2, and 3 were all likely to have a lower quality of life than latent Class 4. This suggests that the higher the quality of life, the more likely it is to be classified into latent Class 4, which means that the lifestyles of the study participants classified in this stratum consisted of healthy behaviors and expressions. These results were confirmed to be similar to those of previous studies that reported an association between healthy lifestyle behaviors and quality of life (Nari et al., 2021). Although it has been reported that depression and sleep problems can be induced (Harrington et al., 2010; Shochat, 2012), no association was found between latent lifestyle classes, depression, and sleep quality in this study. This could be a consequence of lifestyle including internal factors such as eating habits, physical activity, and alcohol in terms of health and well-being, as well as external factors such as stress, environment, and social influence (Lopresti et al., 2013; World Health Organization, 2021). Efforts should be made to understand and examine the overall context to better understand a lifestyle that reflects diversity in a complex manner.

The theoretical basis for lifestyle has been reported by Weber (1978) as a profound insight into the basic components of life’s choices and chances and has since been described as a healthy lifestyle (Cockerham, 2021). In the healthy lifestyle, the values aspect that selects and determines the expressed behavior was emphasized, and this was reported to be related to the healthy lifestyle behavior (Abella & Heslin, 1984; Cass et al., 2021). However, in previous studies, there are many cases where the focus is on physical activity, smoking, drinking, and sleep, which are reflected in the risk of health conditions and accompanying results, focusing on the exposure and frequency of behavior. Recently, in quantitative psychology, the mindset or core values are emphasized in explaining individual attitudes and behaviors (Vuong, 2023). Since this means that lifestyle behavior is based on individual values, it has an important context in lifestyle behavior selection and decision-making for health promotion. This study was distinguished when psychological values were focused on different lifestyle attitudes and behaviors and confirmed the difference in the quality of life according to the distinctive features. If we can understand and change the psychological values reflected in the lifestyle, actions for a healthy and better quality of life can be selected and practiced, which will enable healthy lifestyle behavior management.

As this study was conducted on middle-aged and older Adults individuals, the results cannot be generalized to the lifestyles of other age groups. In addition, the YLP-V used to measure the values reflected in the healthy lifestyle should be considered when interpreting it because it does not reflect medical information. In future research, it is necessary to investigate the physical, social, psychological, and environmental factors related to lifestyle, and to explore various pathways related to lifestyle that determine health. In addition, in terms of public health for health promotion, an approach that provides opportunities for experience to increase understanding of lifestyle and to select and decide healthy behaviors will be needed.

Conclusion

This study confirmed that the lifestyle of middle-aged and older Adults individuals is divided into latent classes that reflect individual values. There were differences in depression, sleep quality, and quality of life according to the latent lifestyle class. In addition, the higher the quality of life, the more the participants engaged in activities classified as a type in which interests and opinions were expressed as healthy aspects. Based on this study, it is necessary to increase academic interest in lifestyles, which reflect different values, rather than simple behavioral patterns. In addition, a strategy to examine lifestyles based on values should be attempted for health promotion.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A3A2A02096338).

ORCID iDs

Young-Myoung Lim

Ram-Ah Kim

Ji-Hyuk Park

Data Availability Statement

The datasets used and/or analyzed in this study require separate approval from the IRB (Institutional Review Board).

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