Development and preliminary validation of mental health check-up questionnaire in Korea

Abstract

Background

This study aimed to develop and validate the Mental Health Check-up Questionnaire (MHCQ), a short assessment tool for identifying early signs of mental health issues and evaluating decreases in positive aspects of mental health.

Methods

The MHCQ was developed based on a review of mental health prevalence data and a survey of screening needs in Korea, covering seven problem domains and one positive domain. The positive domain was designed based on the view that mental health includes more than just the absence of symptoms and encompasses resources for dealing with stress and sustaining productivity and well-being. After expert review, the 66-item questionnaire was administered to 500 participants aged 20 to 70.

Results

Factor analyses identified 51 items consistent with the theoretical framework, demonstrating high reliability with an internal consistency ranging from 0.83 to 0.95. Correlations with established mental health measures supported the MHCQ’s convergent and discriminant validity.

Conclusion

The MHCQ demonstrates preliminary evidence as a concise and reliable mental health screening tool. Future work should establish test–retest and clinical validity using larger, integrated health–lifestyle datasets and develop pathways for integrating the MHCQ into healthcare systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-025-03608-w.

Background

Efforts to improve the mental health of general populations are a significant public health concern worldwide, as the personal and societal costs of mental illness are widely recognized. South Korea has had the highest suicide rate globally for several years, with a rate of 25.7 per 100,000 people in 2021, more than double the OECD average of 11.0, and mental health problems account for the highest proportion of suicide motives [1]. In addition, two-thirds of Koreans have experienced a mental health problem in the past year, with 58% of them reporting that it interfered with their daily lives [2].

South Korea’s public health screening system, implemented through the National Health Insurance Service, is considered well-organized due to its high participation rates, broad coverage, and integration with follow-up care. According to the OECD, Korea has one of the highest screening participation rates among member countries, which contributes to the early detection of chronic diseases and the development of cost-effective intervention strategies [3, 4]. Despite concerns about the severity of mental health problems in Korea, mental health screening is a minor component of public health screening [5]. The National Health Checkup currently comprises only a screening test for depression and a brief cognitive function test for those aged 66 and older.

Recently, the government announced a plan to introduce a screening test for young adults that can detect severe mental illnesses such as schizophrenia and bipolar disorder in addition to depression [6]. While this announcement is a positive development in the early detection of severe mental illnesses, it remains insufficient in terms of expanding mental health screening for universal preventive interventions that can improve individuals’ quality of life by detecting and managing a more comprehensive range of mental health problems in the population at an early stage. Therefore, early detection and intervention of mental health problems necessitate incorporating mental health screening into routine health checkups to include a broader range of mental health problems that are not limited to severe mental illness or specific psychiatric disorders.

The main goal of the MHCQ is to identify populations at risk for significant mental health issues early on so that they can be referred for further assessment and professional help. There is a need for a simple and time-efficient screening tool that can detect not only depression or severe mental illness but also a range of common mental health conditions across age groups. Current screening tools for mental health in Korea mainly focus on depression, anxiety, or specific psychiatric disorders [5]. Additionally, there is a need for screening tools that address emerging areas of mental health concern, such as the growing reliance on smartphones among young adults or concerns about cognitive decline among older adults [2].

The MHCQ has a second purpose, which is to help individuals identify vulnerable areas in mental health domains that may affect their daily functioning and quality of life. This is important even if the mental health issue is not severe enough to require in-depth evaluation. The MHCQ also aims to recommend how individuals can improve their mental health. Achieving this requires identifying problematic areas of mental health and deficiencies in positive mental health aspects and suggesting necessary actions.

While scholars widely accept that mental health is not just about being free from illness but also about overall well-being and the ability to cope with life’s stresses and work productively [7–9], attempts to include an assessment of these positive aspects of the evaluation of mental health remain limited [10]. According to the dual-continua model of mental health [11], well-being represents a dimension that is independent yet related to psychological distress. Building on self-determination theory [12], which conceptualizes well-being as an outcome of fulfilled basic psychological needs for autonomy, competence, and relatedness, the positive dimension of mental health should encompass individuals’ subjective evaluations and satisfaction across multiple life domains—such as cognitive functioning, relational support, resilience, and perceived stability.

Moreover, given Korea’s unique sociocultural context—characterized by high stigma surrounding mental illness, collectivist norms discouraging emotional expression, and the rapid expansion of digital technology—there is a pressing need for a screening instrument that reflects the current state of mental health concerns and population needs. Rather than focusing solely on specific psychiatric disorders, the tool should include diverse subdomains such as emotion regulation difficulties, subjective cognitive decline, and problematic smartphone use to capture the broader patterns of mental health in the general population.

This study aimed to develop a brief self-report questionnaire to help individuals assess their mental health. Unlike single-domain screeners (e.g., PHQ-9, GAD-7, WHO-5), the MHCQ provides a brief yet multidimensional profile that encompasses both common mental health problems and positive aspects of mental health. Such a comprehensive tool is expected to facilitate early detection and intervention of mental disorders and to contribute to universal preventive strategies aimed at improving quality of life and mental health. Furthermore, when combined with other physical health indicators and data on lifestyle habits from health check-ups, this information can form the basis for more comprehensive recommendations on mental and physical health [13, 14].

Methods

Development of the MHCQ

The MHCQ was developed through a sequential three-phase process that integrated national mental health survey data, population needs assessment, and expert consultation. Figure 1 presents an overview of the development and preliminary validation procedures.

Fig. 1.

Development and preliminary validation process of the MHCQ Note. MHCQ = mental health check-up questionnaire

Phase 1 – identifying priority domains

Analysis of the 2022 National Mental Health Status Report [2] revealed a high lifetime prevalence of depressive, anxiety, and alcohol-related disorders, with depressive experiences reported by 11.3% of adults. Smart-device overdependence was most common among individuals in their 20 s (31.3%), often co-occurring with suicidal ideation and depression, whereas cognitive impairment became more frequent after age 60. These findings underscored the need for symptom-dimensional screening tools that captured prevalent behavioral and emotional problems extending beyond diagnostic categories.

Phase 2 – assessing population needs

A national online survey of 200 adults (balanced by gender and age) examined perceived priorities across 12 mental-health areas. Depression, anxiety, and sleep problems were consistently identified as the most important domains across age groups; younger adults emphasized smartphone dependence, middle-aged adults highlighted emotional dysregulation and alcohol use, and older adults prioritized cognitive decline.

Phase 3 – item generation and expert review

Six problem domains—depression, anxiety, sleep problems, emotional dysregulation, addiction problems, and subjective cognitive decline—were derived from the preceding phases. In line with perspectives that mental health encompasses positive resources [11, 12], an additional domain assessing subjective health, satisfaction, and cognitive, economic, and psychological resources was included. Based on DSM-5 criteria [15] and operational definitions, the authors developed 73 items across eight domains. Following expert review by four mental-health professionals (one psychiatrist, two clinical psychologists, and one counseling psychologist), seven redundant or ambiguous items were removed, yielding 66 items; three were reverse-scored to detect inconsistent responses.

Preliminary validation of MHCQ

Participants

We aimed to recruit a naturalistic sample of the general adult population in Korea. To this end, we included men and women between the ages of 20 and 70 who were members of MacroVille’s Embrace panel, operated by Embrain, one of Korea’s largest ISO 20,252–certified online research firms. The panel comprises over 1.4 million members and is stratified by age, sex, and region, allowing for balanced sampling. It is widely used in national and academic surveys and considered demographically representative. To enhance inclusiveness, no exclusion or inclusion criteria were applied based on physical or mental health conditions. A total of 500 participants who consented and accessed the survey were included. Quotas were applied to ensure even distribution across gender and five age groups (20s to 60 s). Once the quota for each group was filled, additional responses were not accepted.

Participants provided informed consent before beginning the survey and were told that some questions might be sensitive. They were assured of anonymity and could choose to discontinue participation at any time without penalty. However, only fully completed surveys were recorded by the panel system, resulting in no missing data. To ensure ethical protection, contact information for national mental health hotlines and counseling services was provided at the end of the survey for participants who might experience emotional distress.

The demographic characteristics of the participants are presented in Table 1. This study was approved by the Institutional Review Board of the researcher’s university (IRB number 240419).

Table 1.

Sociodemographic data of participants (N = 500)

Variables	Category	n (%) or M(SD)
Age (years)		44.32 (13.82)
	20–29	100 (20.0)
	30–39	100 (20.0)
	40–49	100 (20.0)
	50–59	100 (20.0)
	60–69	100 (20.0)
Sex	Male	250 (50.0)
	Female	250 (50.0)
Education	High school or below	97 (18.8)
	College	345 (69.0)
	Above college	61 (12.2)
Marital Status	Married	278 (55.6)
	Single	196 (39.2)
	Divorce, Widowed	26 (5.2)
Occupation	Full-time job	337 (67.4)
	Part-time job	36 (7.2)
	Student	30 (6.0)
	Housewife	50 (10.0)
	Unemployed	47 (9.4)
Monthly Household Income	< 2,000,000 KRW	102 (20.4)
	< 4,000,000 KRW	227 (45.4)
	> 4,000,000 KRW	171 (34.2)
Current physical illness	Yes	111 (22.2)
Experience in seeking help for mental health issues	Yes	73 (14.6)

No missing data

Measures

Mental health check-up questionnaire (MHCQ)

The Mental Health Check-Up Questionnaire (MHCQ) is an 8-domain, 66-item questionnaire designed as a screening tool for mental health check-ups and is rated on a 5-point Likert scale. The questionnaire covers eight domains: depression, anxiety, sleep problems, emotional dysregulation, problematic alcohol use, smart device dependence, subjective cognitive decline, and mental health positivity. There are 14 items for depression, 13 for anxiety, 3 for sleep, 6 for emotional dysregulation, 4 for problematic alcohol use, 5 for smart device dependence, 8 for subjective cognitive decline, and 13 for mental health positivity. The questions about depression, anxiety, sleep, and emotion dysregulation were based on the past two weeks, while questions about alcohol use, smart device dependence, and subjective cognitive decline were based on the past year. The questions about mental health positivity did not have a specific time frame and were rated based on the respondent’s current state.

The center for epidemiologic studies depression scale-revised

This scale is a 20-item scale that assesses how often individuals have experienced symptoms associated with depression over the past week, such as restless sleep, poor appetite, and loneliness. It is a translated and standardized version of the scale [16] derived from the scale developed by Radloff [17]. Scores on the Korean version range from 0 to 60, with higher scores indicating more severe depressive symptoms. In a standardization study of the Korean version, the internal consistency was 0.98 [18], and in this study, it was 0.92.

Korean generalized anxiety disorder-7 [19]

The Korean GAD-7 is a self-report test developed to screen for generalized anxiety disorder and to assess the severity of symptoms. It consists of 7 items rated on a 4-point Likert scale. The internal consistency of the Korean version was 0.0.92 in a previous study [20] and 0.93 in the present study.

The self-report version of the panic disorders severity scale in Korea (PDSS-SR) [21]

The self-report version of the PDSS-SR was validated by a clinician interviewing the patient and assessing the severity of panic disorder and related symptoms over the past month using a 7-item, 5-point Likert scale. The internal reliability of the Korean version of the self-report PDSS-SR was 0.88 in previous research [22] and 0.93 in this study.

Korean version of the insomnia severity index [23]

We utilized the Korean-adapted and standardized version of the insomnia severity index, which was developed to evaluate sleep issues, including tossing and turning, difficulty maintaining sleep, and the resulting functional decline. The Korean version consists of 7 items on a 5-point Likert scale, and it has demonstrated strong internal consistency with a reliability coefficient of 0.92 [24]. In our study, the internal consistency was 0.91.

Alcohol use disorder identification test-Korean [25]

This test was developed by the World Health Organization for screening risky drinking and consists of 10 questions on a 5-point Likert scale. The internal consistency of this test was 0.83 in previous research [25] and 0.88 in this study.

Korean smartphone addiction proneness scale for adults [26]

This scale was developed and validated to diagnose smartphone addiction and consists of 15 questions on a 5-point Likert scale. This study used it to ask about smartphones and internet addiction in general. The internal reliability of the scale was 0.81 when it was developed [27], and the internal agreement in this study was 0.90.

Korean dementia screening questionnaire [28]

The Korean Dementia Screening Questionnaire is a dementia screening questionnaire developed to detect patients with dementia at an early stage. It includes a cognitive dysfunction scale along with ischemic and depression scales to assess the likelihood of vascular dementia and the degree of depression. This study employed 15 items to assess cognitive dysfunction. The internal reliability at the time of development was 0.78, and the internal consistency in this study was 0.89.

Korean difficulties in emotion regulation scale [29]

The scale developed by Graz and Roemer [30] to measure emotional dysregulation was adapted and validated by Cho [29]. It consists of 35 items with six subscales: impulse control difficulties, lack of attention to and awareness of emotions, emotional unavailability, lack of emotional clarity, limited access to emotion regulation strategies, and difficulties in engaging in goal-directed behavior. In Cho’s [29] study, the internal consistency of the total scale was 0.92. In the present study, the internal consistency was 0.94.

Korean version of the satisfaction with the life scale [31]

This is a 5-item, 7-point scale developed by Diener et al. [32] to assess overall satisfaction with life. The Korean version has shown an internal reliability of 0.94 [31], mirroring the 0.94 internal reliability in the present study.

Korean world health organization quality of life WHOQOL-BREF) [33]

We used the WHOQOL-BRIEF [34], developed by the World Health Organization and translated and validated by Min et al. [33], to assess the concurrent validity of the positivity dimension. This scale consists of 26 items in five subdomains (global, physical, psychological, social, and environmental) measured on a 4-point Likert scale. The internal reliability of the Korean version of the validation study was 0.89 [33]. In the current study, the internal reliability was 0.95.

Statistical analysis

Descriptive statistics were computed to summarize the demographic characteristics of the participants. To examine the construct validity of the MHCQ, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted. Prior to factor analyses, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were assessed to confirm the appropriateness of the data for factor analysis. For the EFA, data from 250 randomly selected participants were analyzed using maximum likelihood estimation and Oblimin rotation. Factors were extracted using eigenvalues ≥ 1 as an initial criterion, supplemented by theoretical considerations based on the eight conceptual domains, the scree plot, and the proportion of variance explained. Items with communalities < 0.40 were excluded, and only items with factor loadings ≥ 0.40 were retained [35]. The CFA was then performed on the remaining 250 participants to test the adequacy of the factor structure identified in the EFA. Model fit was evaluated using the chi-square test, comparative fit index (CFI), Tucker–Lewis index (TLI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA). Values of CFI and TLI ≥ 0.90 and RMSEA ≤ 0.08 were considered indicative of acceptable fit [36, 37]. Following the CFA, modification indices (MI) were inspected at the item level to identify potential local misfit, and alternative models (i.e., bifactor and higher-order models) were also evaluated for comparative purposes.

After completing the EFA and CFA, the internal consistency of the final scale was assessed using Cronbach’s α with the full sample of 500 participants. Construct reliability and convergent validity were further evaluated using the CFA sample (n = 250) by calculating composite reliability (CR) and average variance extracted (AVE) from standardized factor loadings and residual variances. CR values ≥ 0.70 and AVE values ≥ 0.50 were considered indicative of adequate reliability and convergent validity. Finally, Pearson’s correlation analyses were conducted with the full sample (N = 500) using established instruments for each domain to examine convergent and discriminant validity.

Results

Construct validity

The Kaier–Meyer–Olkin goodness-of-fit index was 0.93, and Barlett’s test for sphericity was χ² (2145, N = 250) = 12539.78, p < 0.001, indicating that the data set was suitable for factor analysis.

An EFA was conducted with data from 250 participants. After extracting factors using the oblimin rotation method with the maximum likelihood estimation, 14 factors were initially identified, accounting for 72.7% of the variance. Two items from the depression domain, one item from anxiety, one item from smart device dependence, and one item from subjective cognitive decline, with a commonality load lower than 0.4, were removed. The scree plot indicated that the total explanatory power did not change significantly after eight factors. Considering the conceptual framework with eight domains, the number of factors was then set to 8, and the factor analysis was conducted again. This time, the total explanatory power of the factors was 65.17%. Finally, only items with factor loadings greater than 0.40 were retained for each factor. As a result, the final version included 51 items: 8 items for depression, 13 items for positive mental health, 4 items for smart device dependence, 4 items for problematic alcohol use, 3 items for sleep problems, 3 items for emotion dysregulation, 7 items for subjective cognitive decline, and 9 items for anxiety. The factor loadings of each item are presented in Table 2, where items are abbreviated for ease of reference.

Table 2.

Factor loading of items across eight factors (n = 250)

Domain	Content	1	2	3	4	5	6	7	8	Communality
D7	feeling like a failure	0.746								0.78
D13	feeling behind compared to others	0.691								0.77
D4	no hope for improvement	0.622								0.65
D12	life feels overwhelming	0.612								0.74
D8	struggling to start or procrastinate tasks	0.603								0.65
D5	thoughts of disappearing or dying	0.558								0.60
D3	decreased motivation(willingness)	0.461								0.46
D6	difficulty making decisions due to overthinking	0.457								0.54
P5	happiness		0.833							0.79
P4	meaningful of life		0.802							0.74
P10	satisfaction of the living condition		0.802							0.63
P3	fulfillment in relationship		0.795							0.63
P2	content with achievement		0.768							0.68
P7	vibrant living		0.757							0.72
P9	supportive family and friends		0.741							0.55
P1	satisfaction of life		0.737							0.68
P6	good health		0.704							0.57
P8	good judgment in decisions		0.699							0.54
P12	resilience to negative events		0.688							0.57
P11	financial stability		0.652							0.51
P13	calm under stress		0.601							0.56
C6	trouble recalling words in conversations			− 0.752						0.63
C4	increased difficulty in locating misplaced items			− 0.701						0.60
C5	noticeable memory decline over the past year			− 0.682						0.60
C2	repetition of stories or purchases due to memory lapses			− 0.664						0.58
C7	difficulty learning new technology			− 0.656						0.53
C1	frequent forgetfulness after routine activities			− 0.621						0.56
C8	getting lost on familiar routes			− 0.544						0.56
Au3	alcohol as stress relief				− 0.829					0.64
Au1	binge drinking				− 0.807					0.62
Au4	disruption of daily life due to drinking				− 0.758					0.63
Au2	alcohol withdrawal symptoms: insomnia, tremors, anxiety				− 0.589					0.55
S1	difficulty achieving restful sleep					− 0.893				0.66
S3	frequent awakenings and trouble resettling					− 0.855				0.81
S2	long time to fall asleep					− 0.775				0.58
Sd2	stress-driven smart device usage						− 0.804			0.67
Sd1	loss of control over smart device usage						− 0.776			0.68
Sd3	anxiety without smart device usage						− 0.689			0.62
Sd5	disruption of daily function due to smart device usage						− 0.626			0.60
Ed4	prolonged unpleasant feeling							− 0.533		0.70
Ed3	delayed resentment							− 0.501		0.59
Ed5	inability to control anger							− 0.473		0.64
Ed1	difficulty managing emotions							− 0.467		0.67
A10	fear of experiencing panic								− 0.607	0.61
A4	sudden anxiety in crowded/high/closed places								− 0.557	0.53
A7	muscle paralysis or numbness								− 0.550	0.49
A5	experiencing abdominal pain or nausea								− 0.544	0.47
A9	chest tightness or heart pain								− 0.539	0.55
A13	feeling of suffocation or impending death								− 0.533	0.56
A6	trembling hands/feet and flushed face								− 0.494	0.49
A8	social anxiety (severe nervousness in social situations)								− 0.468	0.47
A12	interference with daily life due to obsession with trivial matters								− 0.407	0.40

D Depression, P Mental health positivity, Sd Smart device dependence, Au Problematic alcohol use, S Sleep problem, Ed Emotion dysregulation, A Anxiety

^*p < 0.05

^**p < 0.01

^***p < 0.001

The eight-factor structure identified through EFA was subsequently tested using CFA with data from an independent sample of 250 participants. The model fit indices for the eight-factor model were as follows: χ² (1196, N = 250) = 3261.31, p < 0.001, TLI = 0.87, CFI = 0.88, RMSEA = 0.059 (90% CI: 0.067–0.073). Absolute fit indices indicated an acceptable fit, whereas incremental fit indices fell slightly below the conventional cutoff of 0.90. Item-level parameter estimates for the final measurement model are reported in Supplementary Table S1 (standardized factor loadings, standardized residual variances, and item-level R²). All items met the predefined criterion of factor loadings ≥ 0.40. Latent factor correlations (φ) with 95% confidence intervals (based on robust MLR standard errors) are presented in Supplementary Table S2.

To further probe the borderline incremental fit, modification indices (MIs ≥ 10) were inspected at the item level. The largest MIs suggested residual correlations within the same domain, indicating possible item redundancy or local dependence (e.g., P13–P12, MI = 65.78; D13–D6, 16.42; D8–D6, 15.18; D12–D8, 14.26; D4–D3, 12.03; A8–A4, 11.16). In addition, several MI values pointed to potential cross-loadings (e.g., A3 → Depression, 40.60; A3 → Emotion dysregulation, 28.43; D12 → Anxiety, 13.21; C6 → Anxiety, 10.76; P3 → Sleep, 13.58).

Given the high correlations among negative-emotion domains and the theoretical possibility of a general distress factor, two alternative models were also evaluated: (a) a higher-order model (second-order general factor) and (b) a bifactor model (general factor plus eight specific factors). The fit indices for the higher-order model were χ² (1216, N = 250) = 3442.43, p < 0.001, TLI = 0.87, CFI = 0.88, RMSEA = 0.061 (90% CI: 0.058–0.063). The bifactor model yielded χ² (1177, N = 250) = 3304.73, p < 0.001, TLI = 0.87, CFI = 0.87, RMSEA = 0.060 (90% CI: 0.058–0.063). Neither alternative model demonstrated improved fit or parsimony compared to the correlated eight-factor CFA. Accordingly, the correlated eight-factor solution was retained as the final measurement model.

Reliability

The overall internal consistency of the 51 items was 0.89, and the internal consistency coefficients (Cronbach’s α) for each domain were 0.83–0.95. CR, calculated from the CFA standardized loadings and error variances, also exceeded the recommended threshold of 0.70 for all subscales, supporting the reliability of the MHCQ. Descriptive statistics, domain-specific internal consistency, and correlations among the final domains for the total sample (N = 500), along with composite reliability (CR) and average variance extracted (AVE) values based on the CFA sample (N = 250), are presented in Table 3.

Table 3.

Descriptive statistics, domain-specific internal consistency, intercorrelations (N = 500), and CR/AVE (n = 250) of MHCQ domains

Domains	1	2	3	4	5	6	7	8
1. Depression
2. Anxiety	0.70***
3. Sleep problems	0.49***	0.51***
4. Emotion regulation	0.67***	0.64***	0.53***
5. Alcohol problems	0.26***	0.37***	0.28***	0.29***
6. Smart device dependence	0.46***	0.49***	0.29***	0.51***	0.26***
7. Subjective cognitive decline	0.51***	0.58***	0.39***	0.56***	0.33***	0.56***
8. Mental health positivity	−0.62***	−0.39***	−0.29***	−0.41***	−0.10*	−0.26***	−0.31***
M(SD)	17.67 (7.77)	14.66 (6.21)	6.32 (3.12)	6.98 (3.06)	5.52 (2.54)	9.09 (3.84)	13.44 (5.36)	37.13 (11.53)
Skewness	0.81	1.46	0.89	0.64	2.12	0.67	1.05	0.08
Kurtosis	−0.17	2.06	0.00	−0.43	4.56	−0.31	0.71	−0.67
Cronbach’s alpha	0.93	0.89	0.88	0.85	0.83	0.86	0.89	0.95
CR	0.93	0.89	0.89	0.84	0.83	0.86	0.90	0.95
AVE	0.61	0.46	0.72	0.64	0.55	0.61	0.55	0.62

Descriptive statistics, intercorrelations, and Cronbach’s α were based on the total sample (N = 500), whereas CR and AVE were calculated using the CFA subsample (n = 250)

MHCQ Mental Health Check-up Questionnaire, CR Composite Reliability, AVE Average Variance Extracted

 ^*p < 0.05

^***p < 0.001

Convergent and discriminant validity

Correlations were conducted with established scales for each domain to examine the convergent and discriminant validity of the MHCQ. The results are presented in Table 4. The depression domain was most highly correlated with the CES-D, and the anxiety domain with the GAD-7 and PDSS, demonstrating strong convergent validity. Sleep problems, problematic alcohol use, and smart device dependence were also most highly correlated with representative scales that screen for those specific problems. Emotion dysregulation was highly correlated with scores on DERS, as well as with the CES-D and GAD-7. The positivity domain showed the strongest correlations with the SWLS and WHOQOL. At the same time, it was weakly correlated with problematic alcohol use, which generally showed weak correlations with all other scales. Convergent validity was supported by AVE values, most of which were above the recommended cutoff of 0.50. Although the AVE for the anxiety factor fell slightly below 0.50, its CR exceeded 0.70, indicating an acceptable level of convergent validity (See Table 3). In addition, significant correlations with related constructions provided further evidence of convergent validity.

Table 4.

Correlations between domains of MHCQ and related measures (N = 500)

Domains	CES-D	GAD-7	PDSS	ISI-K	DERS	AUDIT-K	S-scale	K-DSQ	SWLS	WHOQOL
1. Depression	0.82***	0.71***	0.40***	0.47***	0.62***	0.10*	0.45***	0.48***	−0.57***	−0.62***
2. Anxiety	0.68***	0.70***	0.60***	0.48***	0.56***	0.15**	0.47***	0.53***	−0.29***	−0.45***
3. Sleep problems	0.54***	0.46***	0.38***	0.83***	0.39***	0.13**	0.29***	0.34***	−0.27***	−0.38***
4. Emotion dysregulation	0.65***	0.66***	0.41***	0.46***	0.61***	0.11*	0.46***	0.46***	−0.37***	−0.44***
5. Problematic alcohol use	0.30***	0.26***	0.19***	0.27***	0.30***	0.69***	0.22***	0.25***	−0.10*	−0.19***
6. Smart device dependence	0.46***	0.47***	0.31***	0.33***	0.46***	0.15**	0.74***	0.44***	−0.24***	−0.31***
7. Subjective cognitive decline	0.55***	0.51***	0.39***	0.42***	0.55***	0.15**	0.54***	0.68***	−0.24***	−0.37***
8. Mental health positivity	−0.65***	−0.51***	−0.26***	−0.32***	−0.52***	−0.07	−0.34***	−0.31***	0.72***	0.81***

MHCQ Mental Health Check-up Questionnaires, CES-D Center for Epidemiologic Studies Depression Scale, GAD-7 Generalized Anxiety Disorder − 7, PDSS Panic Disorder Severity Scale -SR, ISI-K Korean version of the Insomnia Severity Index, DERS Difficulties in Emotional Regulation Scale, AUDIT-K Korean version of Alcohol Use Disorder Identification Test, S-scale Smartphone Addiction Proneness Scale for Adults, Self-report, KDSQ  Korean Dementia Screening Questionnaire, SWLS Satisfaction with the Life Scale, WHOQOL  Korean WHO Quality of Life Scale Abbreviated Version Problems

^*p < 0.05

^**p < 0.01

^***p < 0.001

In terms of discriminant validity, most domains of the MHCQ showed relatively low correlations with scales measuring unrelated constructs, indicating that each domain assessed a distinct aspect of mental health. However, as expected, the depression and anxiety domains exhibited moderate to high correlations with several other symptom-related measures, reflecting the conceptual and empirical overlap commonly found between these constructs.

Discussion

This study aimed to develop a mental health screening tool for use in general population health check-ups and to investigate its initial validity. Mental health checkups are essential not only to screen for mental health problems early and refer them for more in-depth evaluation but also to check for general well-being and positive aspects so that people can pay attention to them and make efforts to improve them. Therefore, we developed a brief Mental Health Checkup Questionnaire (MHCQ) that can be completed in under 10 min. The questionnaire covers seven domains related to mental health problems and one domain related to positive mental health, based on the prevalence and needs identified for mental health issues.

The sociodemographic characteristics of the participants were compared to the overall demographics of South Korea [38]. The sample was broadly representative of the general population, with 53–65% married and a median household income of approximately KRW 40 million annually. Participants had a higher educational attainment (69.0% college graduates) than the Korean average (53%) and a slightly higher employment rate (67.4% vs. 63.2%). Additionally, 14.6% of participants reported having previously received help for mental health problems, slightly higher than the 12.1% reported nationally [2]. This discrepancy may reflect the balanced sampling by gender and age in our study, differing from the national distribution, which includes a larger proportion of older adults. These characteristics also likely reflect the nature of online panel recruitment, which tends to attract participants who are more digitally literate and of relatively higher socioeconomic status. Therefore, this potential bias should be considered when interpreting the generalizability of the findings.

We assessed the questionnaire’s construct validity, internal reliability, and convergent and discriminant validity in the general population. The key findings are summarized and discussed below.

First, exploratory and confirmatory factor analyses were conducted to assess the construct validity of the MHCQ. The initial item pool comprised 66 items across eight conceptual domains; however, following exploratory factor analysis, 21 items were removed due to low communalities or factor loadings below 0.40, resulting in a final version consisting of 51 items. Notably, all three reverse-scored items were excluded due to their weak associations with intended factors and their tendency to load across multiple domains, highlighting known issues with reverse-scored items [39]. Other excluded items included those related to somatic symptoms, such as decreased appetite or fatigue in the depression domain, repeated worries in the anxiety domain due to cross-loading with depression, and economic difficulties associated with smart devices in the domain of smart device dependence.

CFA using an independent dataset supported the eight-factor structure with the remaining 51 items. Although the CFI and TLI values (0.88 and 0.87, respectively) were slightly below the recommended 0.90 threshold, the RMSEA (0.059) and SRMR (0.051) indicated acceptable fit. Given that this study represents an early stage of scale development and preliminary validation, and considering the relatively small sample size relative to the number of items, these results suggest an overall acceptable model fit [37]. Inspection of item-level modification indices indicated potential within-factor item redundancy (local dependency) and suggested possible cross-loadings for several items.

Latent factor correlations also revealed strong associations among the depression, anxiety, and emotion dysregulation domains, indicating substantial interrelatedness. Subjective cognitive decline showed relatively strong correlations with other mental health domains, suggesting that it may index broader mental health difficulties, particularly among older adults. Furthermore, given the strong negative correlations between psychopathology domains (e.g., depression) and the positivity domain, it was necessary to test whether a single general distress factor could account for the covariance among domains [40, 41]. Therefore, both higher-order and bifactor models were examined; however, neither model demonstrated improved fit compared to the correlated eight-factor structure. These results suggest that mental health, as measured by the MHCQ, is best represented as multiple interrelated dimensions rather than as a single overarching distress factor. The emotional, behavioral, cognitive, and positive domains together support the conceptual breadth of the MHCQ and align with the dual-continua perspective [11], which posits that mental health encompasses both the presence of well-being and the absence of psychopathology.

Nevertheless, given the moderate sample size used for the CFA (n = 250), the limited improvement in fit in the more complex higher-order models may partly reflect sample-size constraints rather than conceptual inadequacy. Future research using larger and more diverse samples could further verify the robustness of the eight-factor structure and, with greater statistical power, examine whether a hierarchical organization among the emotional, behavioral, cognitive, and positive domains emerges.

Second, the overall questionnaire had an internal consistency of 0.89, and the subfactors had good internal reliability of 0.83–0.95. Composite reliability (CR) values for all subfactors also exceeded the recommended threshold of 0.70, further supporting the robustness of the measurement. This consistency across α and CR indicates that the tool is reliable despite the relatively small number of items per subdimension.

Third, correlation analyses with established instruments provided additional evidence for the construct validity of the MHCQ. Each domain correlated most strongly with validated measures of the same construct - for example, depression with the CES-D, anxiety with the GAD-7 and PDSS, emotion dysregulation with the DERS, and positivity with the SWLS and WHOQOL - demonstrating clear convergent validity. Most domains showed low correlations with measures of unrelated constructions, supporting discriminant validity. Moderate associations among symptom-based domains such as depression and anxiety were expected and reflect the conceptual overlap commonly observed among internalizing problems. Overall, these findings confirm that the MHCQ captures distinct yet theoretically coherent dimensions of mental health, integrating symptom-based and positive indicators within a unified framework.

Beyond these psychometric findings, two culturally patterned results merit note. First, the weak negative associations between positive mental health and alcohol problems (and weak positive links with other psychopathology) align with prior Korean evidence that moderate drinking patterns are associated with higher health-related quality of life in Korea [42]. Second, while emotional, cognitive, and motivational symptoms of depression loaded clearly on the depression factor, somatic symptoms (e.g., fatigue, loss of appetite) showed low loadings and were excluded, suggesting that psychological distress in Korea may be expressed through somatic complaints that are partly distinct from emotional, cognitive, and motivational symptoms [43].

Taken together, these cultural and symptom-expression patterns underscore the need for assessment tools that are sensitive to contextually salient constructs. A notable limitation of the MHCQ is that it does not include certain dimensions particularly relevant in Korea, such as burnout and trauma-related symptoms—issues that may be especially pertinent in contexts of workplace stress or disaster exposure.

In addition to content-related limitations, several methodological issues should be acknowledged. The present study did not include a test–retest reliability assessment, the sample size for factor analyses was relatively small, and clinical validation for predicting psychiatric disorders was lacking. Although the sample was broadly representative of the general population, its composition and recruitment method may still limit the generalizability of the findings. Moreover, measurement invariance across gender and age was not examined, and future studies should test whether the MHCQ demonstrates stable factor structures across demographic groups. Addressing these methodological limitations will be critical for establishing the robustness and generalizability of the MHCQ.

Future research should therefore aim to strengthen the validity and utility of the instrument. Studies with larger and more diverse samples are needed to confirm the factor structure and reliability of the scale. While the present findings supported the correlated eight-factor model over bifactor and higher-order alternatives, future studies may explore whether multiple higher-order dimensions emerge when applied to larger or more heterogeneous populations. Administering the MHCQ to the same participants at a 2–3-week interval would also allow for the assessment of test–retest stability over time. Moreover, although the tool was developed as a self-report measure, examining its equivalence across different administration formats (e.g., clinician-administered) would help expand its applicability. Finally, predictive validity should be tested within clinical populations to evaluate the sensitivity and specificity of each domain in identifying individuals at risk for psychiatric disorders, thereby enhancing the MHCQ’s potential as a practical screening instrument.

Another important direction for future research is the establishment of optimal cut-off scores. Although preliminary thresholds based on statistical deviations (e.g., 1.5 standard deviations for mild risk and two standard deviations for moderate risk) may be suggested, more precise and clinically meaningful cut-offs should be derived through receiver operating characteristic (ROC) analyses linked to specific clinical diagnoses. Incorporating additional health indicators into these analyses may further refine thresholds for identifying risk levels and areas of vulnerability, thereby facilitating targeted interventions even among non-clinical populations.

Building on this foundational work, future research should extend the validation of the MHCQ by examining its predictive utility and integration within real-world health check-up systems. Longitudinal and multi-domain studies linking mental and physical health indicators could help refine the tool’s ability to monitor mental health trajectories and generate personalized recommendations for preventive care.

From an implementation standpoint, the MHCQ can serve as a practical screening tool not only within public health programs but also in private or workplace-based health check-up settings. Its brevity and multidimensional coverage make it suitable for inclusion in routine health assessments conducted by hospitals, corporate wellness programs, or university health centers, where mental health monitoring is increasingly recognized as essential. For effective application, screening outcomes should be linked to follow-up resources —such as counseling, digital mental health platforms, or stepped-care services—to ensure that identified individuals receive appropriate support. Broader adoption across public and private health systems could facilitate early detection, reduce stigma, and cost-effectively promote population-level mental health literacy.

Conclusion

This study developed and preliminarily validated the MHCQ as a brief, multidimensional screening tool for general health check-ups. The instrument demonstrated sound reliability and construct validity, capturing both mental health problems and positive aspects of well-being. Future work should further confirm its test–retest reliability and predictive validity while identifying strategies for incorporating the MHCQ into both public and private healthcare settings to enable systematic follow-up and personalized mental health promotion.

Abbreviations

MHCQ

Mental Health Check-up Questionnaire

OECD

Organization for Economic Co-operation and Development

PHQ-9

Patient Health Questionnaire -9

GAD-7

Generalized Anxiety Disorder-7

WHO-5

World Health Organization-Five Well-Being Index

ISO 20252

International Organization for Standardization 20252

EFA

Exploratory Factor Analysis

CFA

Confirmatory Factor Analysis

CR

Composite Reliability

AVE

Average Variance Extracted

MI

Modification Indices

Authors’ contributions

Ji Young Choi and Jiyeon Lee contributed to the conceptualization and methodology. Ji Young Choi conducted project administration, data curation, and formal analysis and wrote the original draft. Jiyeon Lee reviewed and edited the manuscript. Both authors reviewed the manuscript.

Funding

This research was funded by MoAdata and Mediage Corporation.

Data availability

The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Inha University’s Institutional Review Board in the Republic of Korea approved this study (IRB number 240419), and all participants provided informed consent. The research was conducted in accordance with the ethical standards outlined in the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.