Ju Young Lee; Kyung Won Kim

doi:10.5720/kjcn.2026.00024

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Research Article
Psychosocial factors related to the stages of change in reducing sugar intake among adults in Seoul, Korea: a cross-sectional study: Ju Young Lee¹⁾, Kyung Won Kim^2),†; Korean Journal of Community Nutrition 2026;31(1):21-35.
DOI: https://doi.org/10.5720/kjcn.2026.00024
Published online: February 28, 2026

¹⁾Graduate Student, Department of Food and Nutrition, Seoul Women’s University, Seoul, Korea

²⁾Professor, Department of Food and Nutrition, Seoul Women’s University, Seoul, Korea

†Corresponding author: Kyung Won Kim Department of Food and Nutrition, Seoul Women’s University, 621 Hwarang-ro, Nowon-gu, Seoul 01797, Korea Tel: +82-2-970-5647 Email: kwkim@swu.ac.kr

• Received: January 15, 2026 • Revised: January 30, 2026 • Accepted: February 5, 2026

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract
INTRODUCTION
METHODS
RESULTS
DISCUSSION
NOTES
REFERENCES

Abstract

Objectives
This study examined the factors associated with stages of change (SOC) in reducing sugar intake among adults, applying the theory of planned behavior.
Methods
An online survey was conducted among adults aged 19–49 years residing in Seoul, Korea. Based on their SOC in reducing sugar intake, participants (n = 380) were categorized into a pre-action group (45.3%) and an action group (54.7%). Statistical analysis was performed using χ2-test, analysis of covariance, and one-way analysis of variance with linear contrast.
Results
The consumption frequency of sugary foods was significantly higher in the pre-action group than in the action group (P < 0.001). Compared with the action group, participants in the pre-action group perceived the advantages of sugar intake more favorably (P < 0.001), perceived the disadvantages less strongly (P = 0.002), and reported greater influence from significant others (P = 0.004). In contrast, participants in the action group agreed less with insufficient knowledge/skills (P < 0.001), had greater control over the facilitating factors of sugar intake (P < 0.001), and had stronger control beliefs in situations promoting sugar intake (P < 0.001). Behavioral beliefs (P < 0.001) and control beliefs (P < 0.001) showed a significant linear trend across the five SOC, whereas subjective norms did not (P = 0.275).
Conclusion
Psychosocial factors related to sugar intake reduction clearly differed between the SOC groups. In the pre-action group, nutrition education should emphasize lowering the perceived benefits of sugar intake while increasing awareness of its adverse consequences. Strengthening the perception of control over sugar intake is important, despite the factors or situations promoting sugar intake. This can be achieved by providing practical tips and developing skills to reduce sugar intake. For the action group, it is necessary to maintain the reduced sugar intake through continual support and encouragement.
Keywords: adult; sugars; factor; theory of planned behavior; stages of change

INTRODUCTION

Sugars serve as an energy source and contribute to sensory satisfaction. They are widely used as sweeteners in food processing and preparation to enhance the sensory appeal and functional properties of food products. However, excessive sugar intake has been linked to an increased risk of chronic diseases, including dental caries, obesity, type 2 diabetes mellitus, metabolic syndrome, and cardiovascular diseases, and negatively affects overall diet quality [1, 2]. The Korean Dietary Reference Intakes recommend that total sugar intake be limited to 10%–20% of total energy intake, with emphasis on limiting added sugars that are incorporated during food processing or preparation to less than 10% of total energy intake [3]. However, the recent Korea National Health and Nutrition Examination Survey (KNHANES) showed that the average daily sugar intake among adults aged 19 years and older in 2022 was 58.0 g, with males consuming 61.6 g and females consuming 54.4 g [4]. In addition, an analysis of the 2019–2021 KNHANES data reported that 34.0% of adults aged 19–34 years and 23.7% of those aged 35–49 years consumed more than 10% of their energy from sugars derived from processed foods [5].

Eating behaviors are shaped by various factors such as nutrition knowledge, personal beliefs, attitudes, and the eating environment. Theory-based research provides a framework for examining factors that influence eating behaviors. The stages of change (SOC), a key construct within the transtheoretical model, views behavior changes as a process moving from the precontemplation to maintenance stages. As individuals progress through these stages, different strategies or interventions need to be applied to match each stage [6]. The theory of planned behavior (TPB) is also used to explain or predict health behaviors and describes how factors are related to behaviors. The TPB states that the intention to perform a behavior is the key predictor of that behavior, and that intention is determined by attitudes, subjective norms, and perceived behavioral control. Each of these three factors is determined by salient beliefs: behavioral, normative, and control beliefs [7]. Attitudes are shaped by behavioral beliefs and evaluations of these beliefs. Subjective norms are formed by perceptions of significant others’ expectations (i.e., normative beliefs) and motivations to comply with their expectations. Perceived behavioral control is influenced by control beliefs and the perceived power of each condition, reflecting how easy or difficult it is to perform [7]. The TPB has been applied to a variety of nutrition and eating behaviors, such as beverage consumption [8, 9], nutrition label use [10], maternal feeding decisions for toddlers [11], healthy eating intentions [12], and evaluation of nutrition interventions [13, 14].

Previous studies on sugar intake in Korea have mainly focused on sugar consumption patterns and the relationship between sugar intake and factors such as nutrition knowledge, attitudes, and eating behaviors among parents of young children, high school students, university students, and adults [15-18]. Only a few studies have examined SOC for sugar intake reduction or psychosocial factors related to sugar intake among Korean adults [19, 20]. There is a lack of research applying the TPB or SOC models to examine sugar consumption and related psychosocial factors in Korean adults.

This study aimed to explore the differences in psychosocial factors associated with the SOC in reducing sugar intake among adults residing in Seoul using the constructs of the TPB (i.e., behavioral beliefs, subjective norms, and control beliefs regarding sugar intake reduction). This study does not test the TPB itself, but rather applies the constructs of the TPB to examine differences according to the SOC in reducing sugar intake. The findings of this study will provide basic data for developing targeted nutrition education and counseling programs to help adults reduce their sugar intake.

METHODS

Ethics statement

The Institutional Review Board of Seoul Women’s University (approval number: SWU IRB-2024A-04) approved the study protocol. Online informed consent describing the purpose and content of the study was obtained from each participant prior to participating in the online survey.

1. Study design

This study used a cross-sectional design. Data were collected through an online survey conducted between May and October 2024. This study was performed in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement (https://www.strobe-statement.org).

2. Study participants

The study participants were adults aged 19–49 years residing in Seoul, Korea. The participants were recruited through six online communities in Seoul: an office worker community, a parenting community, and four regionally verified communities across the eastern, western, southern, and northern areas of Seoul. All the included online communities were open to both males and females.

Recruitment was conducted by posting a notice containing the survey link, and participation was voluntary. Before accessing the questionnaire, potential participants completed an eligibility screening that assessed age (19–49 years) and residence (Seoul). Those who did not meet these criteria were automatically excluded from participating. Eligible participants were presented with an online information sheet detailing the purpose, content, and procedures of the study. Only those who provided informed consent were allowed to respond to the self-administered online questionnaire. To prevent duplicate participation, the survey platform restricted repeated access. Participants who attempted to re-enter the survey after completion received a notification indicating that they had already participated and further access was blocked.

The minimum sample size was estimated as 318 based on a previous study of population proportion [19], assuming a 95% confidence level, 5% margin of error, and 10% loss. A total of 391 participants completed an online survey. After excluding 11 respondents with missing data on major study variables (i.e., subjective norms), 380 participants (97.2%) were included in the statistical analysis.

3. Study content and methods

1) Survey questionnaire

The questionnaire was developed based on the previous literature on sugar intake and its associated factors [20-23]. It comprised sections assessing general information, SOC in reducing sugar intake, consumption frequency of sugary foods, and factors related to sugar intake, including behavioral beliefs, subjective norms, and control beliefs. In this study, sugar intake was examined as the consumption of sugary beverages and snacks based on the KNHANES findings that these foods constitute a major proportion of sugar intake in adults [5, 24].

General information included participants’ age, sex, height, weight, occupation, and primary meal preparer. Body mass index (BMI) was calculated using self-reported height and weight. The SOC in reducing sugar intake were assessed by asking whether participants were currently reducing their consumption of sugary beverages/snacks and, if not, whether they intended to do so, based on the SOC definitions. The participants were classified into one of five stages. The precontemplation stage included those not reducing sugar intake and with no intention to do so within the next six months. The contemplation and preparation stages included those not currently reducing sugar intake but intending to do so within the next six months or the next month, respectively. The action stage included participants who had been reducing sugar intake for less than six months, and the maintenance stage included those who had been doing so for six months or longer [6]. To provide an overall description of the participants, general characteristics were examined across all five SOC. For the major analyses, the participants were subsequently regrouped into two categories: a pre-action group (pre-contemplation, contemplation, and preparation stages) and an action group (action and maintenance stages). This regrouping was performed to reflect differences in behavioral performance and examine the psychosocial factors associated with the initiation and action of sugar intake reduction behaviors.

To assess the participants’ sugar consumption status, they were asked the consumption frequency of sugary foods over the past three months. A list of 23 food items was developed based on major sources of sugar intake among adults aged 19–49 years, identified from the 2021 KNHANES and previous studies [17, 20, 25, 26]. The food list included beverages (7 items), milk and dairy products (6 items), frozen desserts and snacks (3 items), breads and rice cakes (3 items), sugars and sweets (3 items), and sauces (1 item). Participants reported how often they consumed each food based on a serving size provided, using response categories ranging from “never” to “more than three times per day.”

Behavioral beliefs regarding the outcomes of consuming sugary beverages/snacks were developed based on literature, with a total of 15 items [20-23]. Factor analysis identified two subscales: beliefs about the advantages (nine items) and beliefs about the disadvantages (six items) of consuming sugary beverages/snacks. The advantages included items such as “taste,” “quenching thirst,” “convenience,” and “relieving anxiety or stress.” Disadvantages included “tooth decay,” “weight gain,” and “risk of developing diseases.” The Cronbach’s alpha was 0.83 for the total behavioral beliefs, 0.85 for the advantages subscale, and 0.83 for the disadvantages subscale.

Subjective norms were assessed based on normative beliefs and motivation to comply. Significant others were identified as parents, siblings, spouse/partner, children, friends/co-workers, professionals (e.g., doctors, nutritionists), and mass media [10, 21-23]. Normative beliefs were assessed using seven items by asking whether significant others thought that participants should reduce sugar intake, and motivation to comply (seven items) was assessed by asking how much participants intended to follow significant others’ recommendations. The factor analysis did not identify distinct subscales; thus, subjective norms were treated as a single construct. Cronbach’s alpha for subjective norms was 0.94.

Control beliefs were measured using 15 items assessing how difficult or easy participants perceived controlling their sugar intake under various factors and situations [20-23]. Factor analysis categorized control beliefs related to reduce sugar intake (Cronbach’s α = 0.91) into three subscales: lack of knowledge/skills for reducing sugary intake (six items, Cronbach’s α = 0.86), facilitating factors of sugar intake (five items, Cronbach’s α = 0.88), and situations promoting sugar intake (four items, Cronbach’s α = 0.79).

2) Measurement and scoring of items

The items were measured using a 5-point Likert scale. For behavioral beliefs and normative beliefs, the responses ranged from “strongly disagree” (1) to “strongly agree” (5). The motivation to comply items was rated from “not at all” (1) to “very much” (5). For normative belief and motivation to comply items, participants were asked to select “not applicable” if there were not relevant others. Control belief items were assessed using either “very difficult” (1) to “very easy” (5) or “strongly disagree” (1) to “strongly agree” (5).

The total score for behavioral beliefs or control beliefs was calculated by reversing the scores for disadvantages, or negatively worded items reflecting barriers, and summing the scores of all items. Higher total behavioral beliefs and advantages subscale scores, along with lower disadvantages subscale scores, indicated more favorable perceptions toward consuming sugary beverages/snacks. Higher total control beliefs score and higher subscale score for “situations promoting sugar intake,” and lower subscale scores for “lack of knowledge/skills in reducing sugar intake” and “facilitating factors of sugar intake” were indicative of stronger control beliefs toward reducing sugar intake. For subjective norms, the influence of each referent was calculated as the product of the normative belief score and corresponding motivation to comply score. Responses marked as “not applicable” were excluded from item-level calculations. To assess the overall influence of significant others while accounting for variation in applicable referents across participants, the mean subjective norms score was calculated by dividing the sum of item scores by the number of completed items, excluding “not applicable” referents. Higher scores indicate greater perceived support and influence from significant others regarding one’s reducing sugar intake.

4. Statistical analysis

Statistical analyses (n = 380) were performed using SPSS Statistics version 29.0 (IBM Corp.). General characteristics across the five SOC were compared using χ²-tests for categorical variables and one-way analysis of variance (ANOVA) for continuous variables. Comparisons of psychosocial factors, including behavioral beliefs, subjective norms, and control beliefs, were conducted between the pre-action group (pre-contemplation, contemplation, and preparation stages) and the action group (action and maintenance stages). Descriptive statistics, including means, standard deviations, and frequencies of the variables, were calculated.

Differences in variables such as consumption frequency of sugary foods, behavioral beliefs, subjective norms, and control beliefs between the pre-action and action groups were examined using analysis of covariance (ANCOVA), adjusting for general characteristics (e.g., age, sex, BMI, and occupation). Spearman’s correlation analysis was performed as a secondary analysis to examine the association between the consumption frequency of sugary foods and the five SOC. Factor analysis was performed to identify the subscales of behavioral beliefs, subjective norms, and control beliefs. To examine the linear trend of the study variables across the five SOC in reducing sugar intake, one-way analysis of variance (ANOVA) with linear contrasts was performed at both the total and subscale levels of the study variables. The results of the linear trend analyses were evaluated as P for trend. The statistical significance level was set at P < 0.05.

RESULTS

1. General characteristics of participants by the five stages of change in reducing sugar intake

With respect to SOC in reducing sugar intake, the largest proportion was in the maintenance stage (n = 112, 29.5%), followed by the action (n = 96, 25.2%), contemplation (n = 72, 18.9%), preparation (n = 61, 16.1%), and pre-contemplation stages (n = 39, 10.3%). Females accounted for 73.9% of participants. The most prevalent age group was 30–39 years (45.0%), followed by 19–29 years (33.7%) and 40–49 years (21.3%). No significant differences were observed among the five SOC in terms of sex or age. Similarly, there were no significant differences among the five SOC in terms of height, weight, and BMI (Table 1).

Regarding occupation, 63.7% of participants were professionals/office workers, 24.7% were housewives/unemployed/others, and 11.6% were students. The majority of participants reported that they prepared their own meals (67.6%), followed by parents (22.9%) and spouse/friend/others (9.5%). Occupation and the person primarily responsible for meal preparation did not differ significantly among the five SOC. As shown in Table 1, no significant differences in general characteristics were observed across the five SOC. Based on this distribution and the conceptual framework of the SOC, the subsequent results focused on the differences in psychosocial factors between the pre-action and action groups.

2. Consumption frequency of sugary foods by stages of change groups

The consumption frequency of sugary foods, assessed using 23 items with reference serving sizes, over the past three months was converted into weekly consumption frequency (Table 2). Participants reported an average consumption frequency of 25.4 times per week of the 23 sugary foods. Among the food categories, beverages were consumed most frequently (8.5 times/week), followed by milk and dairy products (6.3 times/week), and frozen desserts and snacks (3.9 times/week).

When analyzed by SOC groups, the total weekly consumption frequency of 23 sugary foods was significantly higher in the pre-action group (36.7 times/week) than in the action group (16.1 times/week, P < 0.001). The pre-action group also reported significantly higher consumption frequencies across all food categories, including beverages (P < 0.001), frozen desserts and snacks (P < 0.001), sugars and sweets (P < 0.001), milk and dairy products (P < 0.001), breads and rice cakes (P < 0.001), and sauce (P = 0.031, Table 2). Spearman correlation analysis showed significant inverse correlations between the five SOC (from precontemplation to maintenance stage) in reducing sugar intake and the weekly consumption frequency of 23 sugary foods (ρ = –0.455, P < 0.001). Significant negative correlations with the five SOC were also observed across food categories including beverages (ρ = –0.429, P < 0.001), frozen desserts and snacks (ρ = –0.356, P < 0.001), breads and rice cakes (ρ = –0.308, P < 0.001), sugar and sweets (ρ = –0.296, P < 0.001), and milk and dairy products (ρ = –0.237, P < 0.001, not shown in Table).

3. Behavioral beliefs by stages of change groups

The ANCOVA results accounting for general characteristics showed that the total score for behavioral beliefs regarding the outcomes of sugar intake (15 items, possible score: 15–75) had a mean of 39.5, corresponding to 52.7 out of 100 (Table 3). The pre-action group had a significantly higher total behavioral beliefs score (mean: 42.2) than the action group (mean: 37.2), indicating more favorable perceptions toward sugary beverages/snacks consumption in the pre-action group (P < 0.001). At the subscale level, scores for beliefs regarding advantages of consuming sugary beverages/snacks (nine items, possible score: 9–45) were significantly higher in the pre-action group (mean: 30.6) than in the action group (mean: 26.9, P < 0.001). In contrast, the action group agreed more on the disadvantages of consuming sugary beverages/snacks (six items, possible score: 6–30) than the pre-action group (P = 0.002).

Specifically, compared with the action group, the pre-action group agreed significantly more with the advantage items, such as “convenience” (P < 0.001), “cost less than other beverages and snacks” (P < 0.001), “quenching thirst” (P < 0.001), “taste” (P < 0.001), “increasing efficiency in study/work” (P < 0.001), “variety of menu options” (P = 0.002), “relieving anxiety and stress” (P = 0.007), and “feeling better” (P = 0.018). Conversely, the action group agreed significantly more with the disadvantage items, including “deteriorating skin condition” (P = 0.003), “gaining weight” (P = 0.005), “risk of developing diseases” (P = 0.005), and “imbalanced nutrient intakes” (P = 0.014).

4. Subjective norms by stages of change groups

The mean subjective norms score, accounting for differences in the number of applicable referents across participants (possible score: 1–25), was 10.4, on average (Table 4). After accounting for general characteristics, the pre-action group reported a significantly higher mean subjective norms score (11.1) than the action group (9.9), indicating stronger perceived social influence from significant others to reduce sugar intake (P = 0.004). At each item level, significant differences between the pre-action and action groups were found for children (P < 0.001), parents (P = 0.001), spouse/partner (P = 0.007), friends/co-workers (P = 0.010), and experts (P = 0.012). The pre-action group scored significantly higher on these items than did the action group, suggesting a greater influence of these significant others on reducing sugar intake.

5. Control beliefs by stages of change group

The mean total score for control beliefs related to reducing sugar intake (15 items, possible score: 15–75) was 48.2, equivalent to 64.3 out of 100 after adjusting for general characteristics (Table 5). The action group had a significantly higher total control beliefs score than the pre-action group (51.8 vs. 43.9, P < 0.001). Regarding the subscales, the action group scored significantly higher on the subscale about situations promoting sugar intake (mean: 12.7 vs. 11.1, P < 0.001) and lower on the subscale related to lack of knowledge and skills (P < 0.001) and the subscale regarding facilitating factors for sugar intake (P < 0.001). These results indicated that participants in the action group perceived greater confidence in their ability to control sugar intake across all three subscales.

Significant differences were found between the two groups for all 15 control belief items. Specifically, the action group agreed significantly less with items reflecting barriers such as “lack of nutrition knowledge” (i.e., sugar content in food, reading nutrition labels, P < 0.001), “lack of cooking skills for making tasty low-sugar snacks” (P = 0.003), and “lack of information on places that sell low-sugar beverages/snacks” (P = 0.011), suggesting stronger control beliefs in knowledge and skills reducing sugar intake in the action group. Compared with the pre-action group, the action group showed less agreement with difficulty in reducing sugar intake because of facilitating factors, such as “convenience” (P < 0.001), “tasting good” (P < 0.001), “easy availability” (P < 0.001), “relatively inexpensive” (P < 0.001), and “lack of time for grocery shopping or cooking” (P < 0.001), indicating stronger control beliefs over facilitating factors of sugar intake in the action group. Consistently, in situations promoting sugar intake, such as “choosing beverages/snacks at cafes or restaurants” (P < 0.001), “exposure to advertisements for sugary beverages/snacks in the media” (P < 0.001), “when others consume sugary beverages/snacks” (P < 0.001), and “when I feel anxious or stressed” (P = 0.011), the action group showed significantly higher control beliefs than the pre-action group (Table 5).

6. Relationship between the five stages of change in reducing sugar intake and study variables

The relationship between SOC in reducing sugar intake and the variables at the total score level is presented in Table 6. ANCOVA showed that the five SOC in reducing sugar intake were significantly related to behavioral beliefs (P < 0.001), control beliefs (P < 0.001), and the mean of subjective norms (P < 0.001). A significant linear trend was observed between SOC and behavioral beliefs (P for trend < 0.001), with behavioral beliefs score decreasing from the contemplation to the maintenance stages, indicating progressively less favorable beliefs about sugar consumption according to SOC. The SOC in reducing sugar intake also revealed a significant linear trend with control beliefs (P for trend < 0.001), with control beliefs score increasing from the contemplation to the maintenance stage, suggesting greater perceived control over reducing sugar intake across the SOC. However, no significant linear trend was observed between the five SOC and the mean of subjective norms (P for trend = 0.275, Table 6).

At the subscale level (Table 7), the ANCOVA results indicated that the five SOC in reducing sugar intake were significantly associated with all subscales examined, including two subscales of behavioral beliefs, one subscale of subjective norms, and three subscales of control beliefs (P < 0.001, respectively). A significant linear trend was identified between the SOC in reducing sugar intake and beliefs regarding advantages (P for trend = 0.001) and disadvantages of consuming sugary beverages/snacks (P for trend < 0.001), control beliefs about lack of knowledge and skills (P for trend < 0.001), control beliefs about facilitating factors of sugar intake (P for trend < 0.001), and control beliefs about situations promoting sugar intake (P for trend = 0.031).

DISCUSSION

In this study, 73.9% of participants were females. Although recruitment was conducted through online communities open to both males and females and sites restricted to females were excluded, the higher proportion of females may reflect their greater use of regional online communities. Approximately 54.7% of the participants were classified into the action group, including those in the action and maintenance stages. A similar pattern was observed in a study examining sugar-sweetened beverage (SSB) consumption among college students, in which the maintenance stage (35.1%) accounted for the largest proportion and the pre-contemplation stage (13.0%) represented the smallest proportion [27]. In contrast, a study of female college students reported that the pre-contemplation stage for sugar intake reduction (44.1%) was the most prevalent [19]. This finding differs from that of the present study, in which only 10.3% of participants were classified in the pre-contemplation stage. Such differences might be partly attributable to differences in participant characteristics (e.g., age distribution) or the timing of data collection. For example, increased public awareness of health and nutrition in recent years may have contributed to greater motivation and readiness to reduce sugar intake among the participants in the present study.

Participants in the action group reported a significantly lower consumption frequency of sugary foods across all examined food categories than those in the pre-action group. In addition, a higher SOC for reducing sugar intake was inversely correlated with the reported consumption frequency of sugary foods. These findings suggest the validity of the SOC-based classification in reflecting differences in actual sugar intake behaviors. However, no significant differences in body weight or BMI were observed between the two groups. This may be partly attributable to the assessment of sugar intake based on consumption frequency rather than the actual intake amount (e.g., grams or kcal). Despite the use of reference serving sizes, frequency-based measures may not fully reflect the total sugar or energy intake. However, this finding should not be interpreted to indicate that reducing the frequency of sugar-containing food consumption lacks health benefits. Body weight and BMI are influenced by multiple factors (e.g., energy intake, physical activity, and dietary patterns), and a reduction in sugar intake alone may not produce detectable anthropometric changes, particularly in a cross-sectional study.

Both the pre-action and action groups exhibited relatively high consumption frequency of beverages and dairy products, indicating that these food categories are major contributors to sugar intake regardless of SOC group. These results are consistent with those of previous studies [5, 24, 28]. Analysis of the 2019–2021 KNHANES data reported that beverages accounted for approximately 30% of the total sugar intake among adults aged 19–34 years and 24% among those aged 35–49 years [5]. Similarly, an analysis of the 2016–2018 KNHANES data found that nearly half of the daily sugar intake among adults aged 19–49 years was derived from ultra-processed foods, including SSB, sweetened milk products, cookies and snacks [24]. These findings suggest that nutrition education focusing on reducing the consumption of SSB, sweet bakery products, and snacks may be effective in lowering sugar intake in adults.

There were clear differences between the SOC groups in terms of their perceptions of outcomes associated with sugar intake. Compared to the action group, participants in the pre-action group viewed the advantages of sugar consumption, such as convenience, low cost, quenching thirst, and taste, more favorably. In contrast, the pre-action group agreed less with the perceived disadvantages of sugar intake such as skin deterioration, weight gain, increased risk of diseases, and nutritional imbalance. Trend analysis further demonstrated a significant linear trend in behavioral beliefs across the five SOC regarding reducing sugar intake, suggesting an association between SOC and individual beliefs about the outcomes of sugar consumption.

Consistent with our study, commonly cited advantages of consuming SSB included taste, energy provision, low cost, and hydration, whereas perceived disadvantages included high sugar content, excessive intake of caffeine, and disease risk in focus group interviews with U.S. adults [22]. However, a study with adults reported some differences in perceptions (e.g., lack of palatability when reducing sugar intake) across sugary food consumption groups, whereas some perceptions (e.g., disease prevention, such as obesity and diabetes mellitus) did not differ significantly between the groups [20], partly supporting the present results. In addition, a study among college students identified perceptions of behavioral outcomes as a key factor in distinguishing SOC groups in adequate sodium intake [29]. Taken together, these findings indicate that behavioral beliefs play an important role in distinguishing the SOC in sugar reduction. Accordingly, nutrition education for the pre-action group may aim to attenuate favorable perceptions of sugar intake by introducing healthy food and beverage alternatives that fulfill similar functions, such as ease of consumption, quenching thirst, and taste. It is also necessary to emphasize both the short- and long-term negative consequences of excessive sugar intake, such as tooth decay, deterioration of diet quality, and increased risk of chronic diseases.

With respect to subjective norms, the pre-action group perceived more pressure to reduce sugar intake from significant others, including children, parents, spouse/partner, friends/co-workers, and experts, than the action group. This finding may reflect greater concern among the surrounding individuals who perceive the participant’s sugar consumption to be relatively high or problematic. Similarly, Kassem et al. [21] identified parents and friends as key social referents influencing carbonated beverage consumption among female adolescents, with parents exerting a strong influence on adolescent behavior. Riebl et al. [9] reported that subjective norms play a substantial role in adolescent SSB intake. In addition, a previous study reported that individuals in the pre-action stage experienced greater pressure from others to consume adequate levels of sodium [29]. However, the linear trend analysis in the present study did not reveal a significant progression in subjective norms score across the five SOC. Thus, the influence of subjective norms on reducing sugar intake was relatively limited.

The findings regarding control beliefs indicate that those in the action group perceived greater confidence in their ability to control sugar intake. These results are consistent with those of previous studies that emphasized perceived behavioral control as a key determinant of SSB consumption and other nutrition behavior [8, 9, 30]. Lima et al. [31] also reported that higher self-efficacy for healthy dietary behaviors was related to lower odds of SSB consumption, supporting the importance of self-efficacy in sugar intake behaviors.

More specifically, the action group agreed significantly less on items addressing the perceived lack of knowledge and skills for reducing sugar intake, compared with the pre-action group. This finding suggests that sufficient nutrition knowledge and practical skills may help overcome the perceived barriers to reducing sugar intake. Thus, nutrition education that focuses on building concrete skills related to sugar intake reduction and increasing nutrition knowledge may be relevant in supporting one’s perception of control over reducing sugar intake. Similarly, Choi & Kim [19] showed that individuals in the pre-contemplation stage of sugar intake reduction exhibited lower nutrition knowledge and poorer sugar-related eating behaviors than those in the action or maintenance stages. Following the mobile-based intervention, significant improvements in nutrition knowledge and favorable behavior changes regarding sugar intake reduction were noted across all SOC groups [19].

Factors facilitating sugar intake, including convenience, taste, ease of access, low cost, and limited time for grocery shopping or cooking, were also identified as important barriers to reducing sugar intake. In addition, participants in the action group reported greater control in situations that promoted sugar intake such as eating out, exposure to advertisements for sugary foods, social contexts, and emotional stress. Similarly, another study found that adults with higher sugar intake exhibited lower self-efficacy for avoiding sweet foods in high-risk situations [20], whereas those in the action stage demonstrated greater confidence in adopting alternative behaviors and managing recommended dietary behaviors across various situations than those in the pre-action stage [29]. Therefore, nutrition education may benefit from focusing on coping methods to deal with the factors or situations that promote sugar intake. Nutrition education might include guidance on choosing or preparing tasty low-sugar snacks, providing information on affordable low-sugar beverages/snacks, and skill building for preparing snacks through hands-on training or online program. An individualized approach can help identify barriers that lead to excessive sugar intake and develop strategies to address these factors.

Although a significant linear trend was observed for behavioral beliefs and control beliefs across the SOC, the precontemplation stage did not follow the same pattern observed from the contemplation to maintenance stages. This finding differs from that of a previous study that reported a clear and consistent linear trend across TPB constructs according to food literacy levels [12]. In the present study, scores for behavioral beliefs or control beliefs in the pre-contemplation stage were positioned between those in the preparation and action stages. This finding suggests that psychosocial changes related to sugar intake reduction may become more apparent after individuals begin to consider behavior changes.

Limitations

This study had some limitations. The participants were adults aged 19–49 years residing in Seoul, and 73.9% of them were females. Thus, the findings may not be generalizable to populations with different age and sex distributions, or to those with distinct geographic or sociodemographic characteristics. Although the consumption frequency questionnaire included a reference serving size to assist with more accurate responses, the extent to which the participants correctly recognized the serving size could not be confirmed. In addition, the cross-sectional design precludes causal inferences between SOC and study variables. Therefore, future studies need to include more diverse demographics, incorporate visual aids (e.g., portion size images) into food frequency questionnaires, quantify sugar intake, if possible, and employ longitudinal study designs to better examine the factors related to sugar reduction behaviors.

Conclusion

This study indicated that participants differed meaningfully in behavioral beliefs, control beliefs, and subjective norms related to sugar intake reduction across SOC, highlighting the necessity for nutrition education and counseling tailored to SOC. For the pre-action group, educational approaches should emphasize weakening favorable perceptions of sugar intake by offering healthy alternatives and practical recipes while strengthening awareness of the negative consequences of excessive sugar intake. Improving knowledge and practical skills, along with training on how to cope with factors or situations that promote sugar intake, are essential for enhancing the perception of control over sugar intake reduction. Support from family members and significant others may further facilitate the motivation for change. Strategies for the action group should focus on reinforcing and sustaining healthy behaviors related to sugar intake.

NOTES

CONFLICT OF INTEREST

There are no financial or other issues that might lead to conflict of interest.

FUNDING

This work was supported by a research grant from Seoul Women’s University (2025-0250).

ACKNOWLEDGEMENTS

We are grateful to the participants of this study.

DATA AVAILABILITY

The study participants did not provide written consent for their data to be shared publicly. Due to the sensitive nature of this research, supporting data are not available.

Table 1.

General characteristics of participants by the stages of change in reducing sugar intake

Variables	Total (n = 380)	Stages of change					P-value¹⁾
Variables	Total (n = 380)	Precontemplation (n = 39)	Contemplation (n = 72)	Preparation (n = 61)	Action (n = 96)	Maintenance (n = 112)	P-value¹⁾
Sex
Male	99 (26.1)	15 (38.5)	16 (22.2)	16 (26.2)	23 (24.0)	29 (25.9)	0.422
Female	281 (73.9)	24 (61.5)	56 (77.8)	45 (73.8)	73 (76.0)	83 (74.1)
Age (year)
19–29	128 (33.7)	18 (46.2)	23 (31.9)	19 (31.1)	29 (30.2)	39 (34.8)	0.686
30–39	171 (45.0)	12 (30.8)	35 (48.6)	29 (47.5)	48 (50.0)	47 (42.0)
40–49	81 (21.3)	9 (23.1)	14 (19.4)	13 (21.3)	19 (19.8)	26 (23.2)
Height (cm)
Male	175.6 ± 4.8	173.5 ± 4.8	177.2 ± 4.0	176.9 ± 4.2	174.3 ± 5.2	176.3 ± 4.8	0.078
Female	162.8 ± 4.5	162.5 ± 3.4	163.1 ± 5.0	163.9 ± 3.9	162.9 ± 4.5	162.1 ± 4.6	0.312
Weight (kg)
Male	76.1 ± 9.9	76.2 ± 11.3	77.6 ± 10.4	74.7 ± 7.8	75.9 ± 11.3	76.3 ± 9.2	0.952
Female	57.2 ± 10.7	54.2 ± 6.5	57.6 ± 8.8	57.4 ± 8.6	58.9 ± 15.2	56.2 ± 8.7	0.335
Body mass index (kg/m²)
Male	24.7 ± 3.0	25.3 ± 3.8	24.7 ± 3.1	23.8 ± 2.0	24.9 ± 3.0	24.6 ± 3.0	0.715
Female	21.6 ± 3.9	20.5 ± 2.4	21.7 ± 3.3	21.4 ± 3.0	22.2 ± 5.5	21.4 ± 3.2	0.425
Occupation
Students	44 (11.6)	7 (17.9)	7 (9.7)	9 (14.8)	10 (10.4)	11 (9.8)	0.084
Professionals/office workers	242 (63.7)	17 (43.6)	41 (56.9)	39 (63.9)	66 (68.8)	79 (70.5)
Housewives/unemployed/others	94 (24.7)	15 (38.5)	24 (33.3)	13 (21.3)	20 (20.8)	22 (19.6)
Meal preparer
Self	257 (67.6)	27 (69.2)	51 (70.8)	41 (67.2)	62 (64.6)	76 (67.9)	0.731
Parents	87 (22.9)	9 (23.1)	15 (20.8)	13 (21.3)	28 (29.2)	22 (19.6)
Spouse/friend/others	36 (9.5)	3 (7.7)	6 (8.3)	7 (11.5)	6 (6.3)	14 (12.5)

n (%) or Mean ± SD.

¹⁾By χ²-test or analysis of variance (ANOVA).

Table 2.

Consumption frequency of sugary foods by the stages of change in reducing sugar intake

Variables	Total (n = 380)	Stages of change		P-value¹⁾
Variables	Total (n = 380)	Pre-action group (n = 172)	Action group (n = 208)	P-value¹⁾
Beverages (7 items)²⁾	8.5 ± 9.5³⁾	12.2 ± 10.7	5.4 ± 7.1	< 0.001
Milk and dairy products (6 items)	6.3 ± 7.9	8.9 ± 10.3	4.2 ± 4.2	< 0.001
Frozen desserts and snacks (3 items)	3.9 ± 5.1	5.9 ± 6.1	2.2 ± 3.4	< 0.001
Breads and rice cakes (3 items)	2.7 ± 5.5	3.9 ± 6.0	1.8 ± 5.0	< 0.001
Sugars and sweets (3 items)	2.7 ± 4.1	4.2 ± 5.3	1.5 ± 1.9	< 0.001
Sauce (1 item)	1.4 ± 2.0	1.6 ± 2.3	1.2 ± 1.7	0.031
Total (23 items)	25.4 ± 25.8	36.7 ± 30.4	16.1 ± 16.1	< 0.001

Mean ± SD.

¹⁾By analysis of covariance (ANCOVA), adjusted for sex, age, BMI, and occupation.

²⁾The consumption frequency of each food item was measured using nine response categories from “never” to “more than three times per day.”

³⁾Summated consumption frequency of sugary foods in each food group per week.

Table 3.

Behavioral beliefs regarding sugar intake of participants by the stages of change in reducing sugar intake

Variables	Total (n = 380)	Stages of change		P-value¹⁾
Variables	Total (n = 380)	Pre-action group (n = 172)	Action group (n = 208)	P-value¹⁾
If I consume sugary beverages/snacks
1. It will taste good²⁾	3.5 ± 1.1	3.8 ± 1.0	3.3 ± 1.1	< 0.001
2. It will quench my thirst (e.g., carbonated beverages, sports drinks, fruit juices, etc.)	2.5 ± 1.3	2.8 ± 1.2	2.3 ± 1.2	< 0.001
3. It will be convenient to eat	3.3 ± 1.2	3.7 ± 1.0	2.9 ± 1.2	< 0.001
4. It will help relieve my anxiety and stress	3.4 ± 1.1	3.6 ± 1.1	3.3 ± 1.1	0.007
5. It will make me feel better	3.6 ± 1.1	3.7 ± 1.1	3.5 ± 1.0	0.018
6. It will increase my efficiency when I study or work	3.3 ± 1.1	3.5 ± 1.0	3.1 ± 1.1	< 0.001
7. It will cost less than other beverages and snacks	2.7 ± 1.3	3.0 ± 1.3	2.4 ± 1.2	< 0.001
8. It will provide carbohydrates and energy	3.2 ± 1.2	3.3 ± 1.2	3.2 ± 1.2	0.418
9. Variety of menu options will be available	3.1 ± 1.2	3.4 ± 1.2	3.0 ± 1.2	0.002
10. Tooth decay will occur	4.1 ± 1.0	4.0 ± 1.0	4.1 ± 1.1	0.235
11. I will gain weight	4.5 ± 0.8	4.4 ± 0.9	4.6 ± 0.8	0.005
12. The risk of developing diseases (e.g., diabetes mellitus, heart disease) will increase	4.5 ± 0.8	4.4 ± 0.8	4.6 ± 0.7	0.005
13. My skin condition will deteriorate	4.1 ± 0.9	3.9 ± 1.0	4.2 ± 0.9	0.003
14. My meal patterns will become irregular	3.9 ± 1.0	3.8 ± 1.0	4.0 ± 1.0	0.065
15. My nutrient intakes will become imbalanced	4.1 ± 0.8	4.0 ± 0.9	4.2 ± 0.8	0.014
Beliefs regarding advantages of consuming sugary beverages/snacks³⁾	28.6 ± 7.0	30.6 ± 6.9	26.9 ± 6.6	< 0.001
Beliefs regarding disadvantages of consuming sugary beverages/snacks⁴⁾	25.1 ± 4.0	24.4 ± 4.2	25.7 ± 3.7	0.002
Total behavioral beliefs score⁵⁾	39.5 ± 7.4	42.2 ± 6.5	37.2 ± 7.4	< 0.001

Mean ± SD.

¹⁾By ANCOVA, adjusted for sex, age, BMI, and occupation.

²⁾Each item was measured by five-point scale from “strongly disagree” (1) to “strongly agree” (5).

³⁾Subscale score for nine items (Items 1–9), possible score: 9–45. Higher scores indicated stronger agreement with the advantages of consuming sugary beverages/snacks.

⁴⁾Subscale score for six items (Items 10–15), possible score: 6–30. Higher scores indicated stronger agreement with the disadvantages of consuming sugary beverages/snacks.

⁵⁾Total score for 15 items, possible score: 15–75. The items assessing disadvantages were scored in reverse. Higher scores indicated more favorable beliefs about consuming sugary beverages/snacks.

Table 4.

Subjective norms regarding sugar intake of participants by the stages of change in reducing sugar intake

Variables	Total		Stages of change				P-value¹⁾
Variables	Total		Pre-action group		Action group		P-value¹⁾
Normative belief X motivation to comply²⁾
1. Parents	363³⁾	10.8 ± 5.7	169	11.8 ± 6.0	194	10.0 ± 5.4	0.001
2. Siblings	337	9.4 ± 5.8	152	10.0 ± 5.5	185	8.9 ± 5.9	0.055
3. Spouse/partner	285	9.8 ± 5.9	118	10.7 ± 6.2	167	9.1 ± 5.6	0.007
4. Children	169	9.0 ± 5.8	78	10.7 ± 6.4	91	7.6 ± 4.8	< 0.001
5. Friends/co-workers	353	8.8 ± 5.2	162	9.5 ± 5.3	191	8.3 ± 5.1	0.010
6. Experts (doctors, nutritionists, etc.)	333	11.6 ± 6.3	144	12.5 ± 6.4	189	10.9 ± 6.1	0.012
7. Mass media (television, social media, internet articles, etc.)	354	11.7 ± 6.3	160	12.3 ± 6.4	194	11.2 ± 6.2	0.079
Mean subjective norms score⁴⁾	380	10.4 ± 4.9	172	11.1 ± 5.1	208	9.9 ± 4.7	0.004

Mean ± SD.

¹⁾By ANCOVA, adjusted for sex, age, BMI, and occupation.

²⁾Possible score per item: 1–25. Scores were calculated by multiplying each normative belief score (from “strongly disagree” (1) to “strongly agree” (5)) by corresponding motivation to comply score (from “not at all” (1) to “very much” (5)). Responses marked as “not applicable” were excluded from item-level calculations.

³⁾Number of participants included in the analysis for each item, excluding responses marked as “not applicable” for either normative belief or motivation to comply.

⁴⁾Possible score: 1–25. The mean subjective norms score reflect the overall influence of significant others, accounting for the differences in the number of applicable items across participants. For each participant, the sum of item scores was divided by the number of completed items, excluding “not applicable” referents from both numerator and denominator. Higher scores indicated greater perceived influence from significant others.

Table 5.

Control beliefs regarding sugar intake of participants by the stages of change in reducing sugar intake

Variables	Total (n = 380)	Stages of change		P-value¹⁾
Variables	Total (n = 380)	Pre-action group (n = 172)	Action group (n = 208)	P-value¹⁾
It is difficult to reduce the intake of sugary beverages/snacks because of...
1. Lack of nutrition knowledge, such as the sugar content in foods²⁾	2.3 ± 1.1	2.6 ± 1.2	2.0 ± 1.0	< 0.001
2. Lack of knowledge in reading and interpreting nutrition labels when purchasing processed foods	2.2 ± 1.2	2.6 ± 1.3	2.0 ± 1.1	< 0.001
3. Lack of cooking skills for making tasty low-sugar snacks	2.7 ± 1.3	2.9 ± 1.3	2.5 ± 1.3	0.003
4. The size of beverages (sweetened coffee, carbonated drinks, etc.) is large	2.5 ± 1.3	2.8 ± 1.3	2.2 ± 1.1	< 0.001
5. Lack of information on places that sell low-sugar beverages/snacks	2.5 ± 1.3	2.6 ± 1.3	2.3 ± 1.3	0.011
6. There are many sugary beverages/snacks at home	2.6 ± 1.3	3.0 ± 1.3	2.3 ± 1.2	< 0.001
7. Sugary beverages/snacks taste good	3.2 ± 1.3	3.6 ± 1.2	2.9 ± 1.2	< 0.001
8. Sugary beverages/snacks are easily available	3.2 ± 1.3	3.5 ± 1.2	2.9 ± 1.3	< 0.001
9. Convenience (easy to prepare, carry and consume on the go)	3.1 ± 1.3	3.5 ± 1.2	2.7 ± 1.3	< 0.001
10. Sugary beverages/snacks are relatively inexpensive	2.7 ± 1.2	3.0 ± 1.2	2.5 ± 1.2	< 0.001
11. Lack of time for grocery shopping or cooking	2.8 ± 1.3	3.1 ± 1.3	2.6 ± 1.3	< 0.001
12. Exposure to advertisements for sugary beverages/snacks in the media	2.7 ± 1.3	3.0 ± 1.3	2.5 ± 1.2	< 0.001
How difficult or easy is it to refrain from consuming sugary beverages/snacks in the following situations?
13. When I feel anxious or stressed	2.8 ± 1.1	2.7 ± 1.1	2.9 ± 1.1	0.011
14. When others (family members, friends) consume sugary beverages/snacks	2.9 ± 1.2	2.7 ± 1.2	3.1 ± 1.2	< 0.001
15. When I choose beverages/snacks at cafes or restaurants	3.0 ± 1.2	2.7 ± 1.2	3.2 ± 1.2	< 0.001
Control beliefs about lack of knowledge and skills³⁾	14.8 ± 5.8	16.5 ± 6.1	13.4 ± 5.0	< 0.001
Control beliefs about facilitating factors of sugar intake⁴⁾	14.9 ± 5.2	16.6 ± 4.8	13.5 ± 5.2	< 0.001
Control beliefs about situations promoting sugar intake⁵⁾	12.0 ± 3.7	11.1 ± 3.8	12.7 ± 3.5	< 0.001
Total control beliefs score⁶⁾	48.2 ± 12.6	43.9 ± 12.6	51.8 ± 11.4	< 0.001

Mean ± SD.

¹⁾By ANCOVA, adjusted for sex, age, BMI, and occupation.

²⁾Items were measured by five-point scales from “strongly disagree” (1) to “strongly agree” (5), or from “very difficult” (1) to “very easy” (5).

³⁾Subscale score for six items (Items 1–6), possible score: 6–30. Higher scores indicated greater agreement with insufficient knowledge and skills regarding sugar intake.

⁴⁾Subscale score for five items (Items 7–11), possible score: 5–25. Higher scores indicated lower control beliefs regarding factors facilitating sugar intake.

⁵⁾Subscale score for four items (Items 12–15), possible score: 4–20. Item 12 was scored in reverse to calculate the subscale score. Higher scores indicated stronger control beliefs in situations promoting sugar intake.

⁶⁾Total score for 15 items; possible score: 15–75. Items 1–12 were scored in reverse. Higher scores indicated stronger control beliefs regarding sugar intake.

Table 6.

Factors related to the stages of change in reducing sugar intake at the total score level of variables

Variables	Stages of change					P-value¹⁾	>P for trend²⁾
Variables	Precontemplation (n = 39)	Contemplation (n = 72)	Preparation (n = 61)	Action (n = 96)	Maintenance (n = 112)	P-value¹⁾	>P for trend²⁾
Behavioral beliefs³⁾	41.8 ± 6.9	42.4 ± 6.9	42.3 ± 5.9	38.1 ± 7.1	36.5 ± 7.6	< 0.001	< 0.001
Mean of subjective norms⁴⁾	7.8 ± 3.9	11.3 ± 4.5	13.1 ± 5.3	10.8 ± 4.1	9.1 ± 5.0	< 0.001	0.275
Control beliefs⁵⁾	48.6 ± 14.9	40.5 ± 11.4	45.0 ± 11.5	49.1 ± 9.8	54.1 ± 12.1	< 0.001	< 0.001

Mean ± SD.

¹⁾By ANCOVA, adjusted for sex, age, BMI, and occupation.

²⁾By one-way ANOVA with linear contrast.

³⁾Total score for 15 items; possible score: 15–75. The items assessing disadvantages were scored in reverse. Higher scores indicated more favorable beliefs about consuming sugary beverages/snacks.

⁵⁾Total score for 15 items; possible score: 15–75. Items 1–12 were scored in reverse. Higher scores indicated stronger control beliefs regarding sugar intake.

Table 7.

Factors related to the stages of change in reducing sugar intake at the subscale score level of variables

Variables	Stages of change					P-value¹⁾	P for trend²⁾
Variables	Precontemplation (n = 39)	Contemplation (n = 72)	Preparation (n = 61)	Action (n = 96)	Maintenance (n = 112)	P-value¹⁾	P for trend²⁾
Beliefs regarding advantages of consuming sugary beverages/snacks³⁾	28.6 ± 7.7	31.6 ± 6.5	30.9 ± 6.6	27.6 ± 6.0	26.3 ± 7.0	< 0.001	0.001
Beliefs regarding disadvantages of consuming sugary beverages/snacks⁴⁾	22.7 ± 5.1	25.2 ± 3.7	24.5 ± 3.7	25.5 ± 3.0	25.8 ± 4.3	< 0.001	< 0.001
Mean of subjective norms⁵⁾	7.8 ± 3.9	11.3 ± 4.5	13.1 ± 5.3	10.8 ± 4.1	9.1 ± 5.0	< 0.001	0.275
Control beliefs about lack of knowledge and skills⁶⁾	14.8 ± 6.1	17.7 ± 6.1	16.2 ± 6.0	14.8 ± 4.6	12.2 ± 5.1	< 0.001	< 0.001
Control beliefs about facilitating factors of sugar intake⁷⁾	15.2 ± 5.8	17.8 ± 4.2	16.2 ± 4.6	14.5 ± 4.6	12.7 ± 5.5	< 0.001	< 0.001
Control beliefs about situations promoting sugar intake⁸⁾	12.6 ± 4.5	9.9 ± 3.6	11.4 ± 3.1	12.4 ± 3.4	13.0 ± 3.6	< 0.001	0.031

Mean ± SD.

¹⁾By ANCOVA, adjusted for sex, age, BMI, and occupation.

²⁾By one-way ANOVA with linear contrast.

³⁾Subscale score for nine items, possible score: 9–45. Higher scores indicated stronger agreement with the advantages of consuming sugary beverages/snacks.

⁴⁾Subscale score for six items, possible score: 6–30. Higher scores indicated stronger agreement with the disadvantages of consuming sugary beverages/snacks.

⁵⁾Possible score: 1–25. The mean subjective norms score reflect the overall influence of significant others, accounting for the differences in the number of applicable items across participants. For each participant, the sum of item scores was divided by the number of completed items, excluding “not applicable” referents from both numerator and denominator. Higher scores indicated greater perceived influence from significant others.

⁶⁾Subscale score for six items, possible score: 6–30. Higher scores indicated greater agreement with insufficient knowledge and skills regarding sugar intake.

⁷⁾Subscale score for five items, possible score: 5–25. Higher scores indicated lower control beliefs regarding factors facilitating sugar intake.

⁸⁾Subscale score for four items, possible score: 4–20. Item 12 was scored in reverse to calculate the subscale score. Higher scores indicated stronger control beliefs in situations promoting sugar intake.

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Psychosocial factors related to the stages of change in reducing sugar intake among adults in Seoul, Korea: a cross-sectional study

Korean J Community Nutr. 2026;31(1):21-35. Published online February 28, 2026

DOI: https://doi.org/10.5720/kjcn.2026.00024

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Psychosocial factors related to the stages of change in reducing sugar intake among adults in Seoul, Korea: a cross-sectional study

Variables	Total (n = 380)	Stages of change					P-value¹⁾
Variables	Total (n = 380)	Precontemplation (n = 39)	Contemplation (n = 72)	Preparation (n = 61)	Action (n = 96)	Maintenance (n = 112)	P-value¹⁾
Sex
Male	99 (26.1)	15 (38.5)	16 (22.2)	16 (26.2)	23 (24.0)	29 (25.9)	0.422
Female	281 (73.9)	24 (61.5)	56 (77.8)	45 (73.8)	73 (76.0)	83 (74.1)
Age (year)
19–29	128 (33.7)	18 (46.2)	23 (31.9)	19 (31.1)	29 (30.2)	39 (34.8)	0.686
30–39	171 (45.0)	12 (30.8)	35 (48.6)	29 (47.5)	48 (50.0)	47 (42.0)
40–49	81 (21.3)	9 (23.1)	14 (19.4)	13 (21.3)	19 (19.8)	26 (23.2)
Height (cm)
Male	175.6 ± 4.8	173.5 ± 4.8	177.2 ± 4.0	176.9 ± 4.2	174.3 ± 5.2	176.3 ± 4.8	0.078
Female	162.8 ± 4.5	162.5 ± 3.4	163.1 ± 5.0	163.9 ± 3.9	162.9 ± 4.5	162.1 ± 4.6	0.312
Weight (kg)
Male	76.1 ± 9.9	76.2 ± 11.3	77.6 ± 10.4	74.7 ± 7.8	75.9 ± 11.3	76.3 ± 9.2	0.952
Female	57.2 ± 10.7	54.2 ± 6.5	57.6 ± 8.8	57.4 ± 8.6	58.9 ± 15.2	56.2 ± 8.7	0.335
Body mass index (kg/m²)
Male	24.7 ± 3.0	25.3 ± 3.8	24.7 ± 3.1	23.8 ± 2.0	24.9 ± 3.0	24.6 ± 3.0	0.715
Female	21.6 ± 3.9	20.5 ± 2.4	21.7 ± 3.3	21.4 ± 3.0	22.2 ± 5.5	21.4 ± 3.2	0.425
Occupation
Students	44 (11.6)	7 (17.9)	7 (9.7)	9 (14.8)	10 (10.4)	11 (9.8)	0.084
Professionals/office workers	242 (63.7)	17 (43.6)	41 (56.9)	39 (63.9)	66 (68.8)	79 (70.5)
Housewives/unemployed/others	94 (24.7)	15 (38.5)	24 (33.3)	13 (21.3)	20 (20.8)	22 (19.6)
Meal preparer
Self	257 (67.6)	27 (69.2)	51 (70.8)	41 (67.2)	62 (64.6)	76 (67.9)	0.731
Parents	87 (22.9)	9 (23.1)	15 (20.8)	13 (21.3)	28 (29.2)	22 (19.6)
Spouse/friend/others	36 (9.5)	3 (7.7)	6 (8.3)	7 (11.5)	6 (6.3)	14 (12.5)

Variables	Total (n = 380)	Stages of change		P-value¹⁾
Variables	Total (n = 380)	Pre-action group (n = 172)	Action group (n = 208)	P-value¹⁾
Beverages (7 items)²⁾	8.5 ± 9.5³⁾	12.2 ± 10.7	5.4 ± 7.1	< 0.001
Milk and dairy products (6 items)	6.3 ± 7.9	8.9 ± 10.3	4.2 ± 4.2	< 0.001
Frozen desserts and snacks (3 items)	3.9 ± 5.1	5.9 ± 6.1	2.2 ± 3.4	< 0.001
Breads and rice cakes (3 items)	2.7 ± 5.5	3.9 ± 6.0	1.8 ± 5.0	< 0.001
Sugars and sweets (3 items)	2.7 ± 4.1	4.2 ± 5.3	1.5 ± 1.9	< 0.001
Sauce (1 item)	1.4 ± 2.0	1.6 ± 2.3	1.2 ± 1.7	0.031
Total (23 items)	25.4 ± 25.8	36.7 ± 30.4	16.1 ± 16.1	< 0.001

Variables	Total (n = 380)	Stages of change		P-value¹⁾
Variables	Total (n = 380)	Pre-action group (n = 172)	Action group (n = 208)	P-value¹⁾
If I consume sugary beverages/snacks
1. It will taste good²⁾	3.5 ± 1.1	3.8 ± 1.0	3.3 ± 1.1	< 0.001
2. It will quench my thirst (e.g., carbonated beverages, sports drinks, fruit juices, etc.)	2.5 ± 1.3	2.8 ± 1.2	2.3 ± 1.2	< 0.001
3. It will be convenient to eat	3.3 ± 1.2	3.7 ± 1.0	2.9 ± 1.2	< 0.001
4. It will help relieve my anxiety and stress	3.4 ± 1.1	3.6 ± 1.1	3.3 ± 1.1	0.007
5. It will make me feel better	3.6 ± 1.1	3.7 ± 1.1	3.5 ± 1.0	0.018
6. It will increase my efficiency when I study or work	3.3 ± 1.1	3.5 ± 1.0	3.1 ± 1.1	< 0.001
7. It will cost less than other beverages and snacks	2.7 ± 1.3	3.0 ± 1.3	2.4 ± 1.2	< 0.001
8. It will provide carbohydrates and energy	3.2 ± 1.2	3.3 ± 1.2	3.2 ± 1.2	0.418
9. Variety of menu options will be available	3.1 ± 1.2	3.4 ± 1.2	3.0 ± 1.2	0.002
10. Tooth decay will occur	4.1 ± 1.0	4.0 ± 1.0	4.1 ± 1.1	0.235
11. I will gain weight	4.5 ± 0.8	4.4 ± 0.9	4.6 ± 0.8	0.005
12. The risk of developing diseases (e.g., diabetes mellitus, heart disease) will increase	4.5 ± 0.8	4.4 ± 0.8	4.6 ± 0.7	0.005
13. My skin condition will deteriorate	4.1 ± 0.9	3.9 ± 1.0	4.2 ± 0.9	0.003
14. My meal patterns will become irregular	3.9 ± 1.0	3.8 ± 1.0	4.0 ± 1.0	0.065
15. My nutrient intakes will become imbalanced	4.1 ± 0.8	4.0 ± 0.9	4.2 ± 0.8	0.014
Beliefs regarding advantages of consuming sugary beverages/snacks³⁾	28.6 ± 7.0	30.6 ± 6.9	26.9 ± 6.6	< 0.001
Beliefs regarding disadvantages of consuming sugary beverages/snacks⁴⁾	25.1 ± 4.0	24.4 ± 4.2	25.7 ± 3.7	0.002
Total behavioral beliefs score⁵⁾	39.5 ± 7.4	42.2 ± 6.5	37.2 ± 7.4	< 0.001

Variables	Total		Stages of change				P-value¹⁾
Variables	Total		Pre-action group		Action group		P-value¹⁾
Normative belief X motivation to comply²⁾
1. Parents	363³⁾	10.8 ± 5.7	169	11.8 ± 6.0	194	10.0 ± 5.4	0.001
2. Siblings	337	9.4 ± 5.8	152	10.0 ± 5.5	185	8.9 ± 5.9	0.055
3. Spouse/partner	285	9.8 ± 5.9	118	10.7 ± 6.2	167	9.1 ± 5.6	0.007
4. Children	169	9.0 ± 5.8	78	10.7 ± 6.4	91	7.6 ± 4.8	< 0.001
5. Friends/co-workers	353	8.8 ± 5.2	162	9.5 ± 5.3	191	8.3 ± 5.1	0.010
6. Experts (doctors, nutritionists, etc.)	333	11.6 ± 6.3	144	12.5 ± 6.4	189	10.9 ± 6.1	0.012
7. Mass media (television, social media, internet articles, etc.)	354	11.7 ± 6.3	160	12.3 ± 6.4	194	11.2 ± 6.2	0.079
Mean subjective norms score⁴⁾	380	10.4 ± 4.9	172	11.1 ± 5.1	208	9.9 ± 4.7	0.004

Variables	Total (n = 380)	Stages of change		P-value¹⁾
Variables	Total (n = 380)	Pre-action group (n = 172)	Action group (n = 208)	P-value¹⁾
It is difficult to reduce the intake of sugary beverages/snacks because of...
1. Lack of nutrition knowledge, such as the sugar content in foods²⁾	2.3 ± 1.1	2.6 ± 1.2	2.0 ± 1.0	< 0.001
2. Lack of knowledge in reading and interpreting nutrition labels when purchasing processed foods	2.2 ± 1.2	2.6 ± 1.3	2.0 ± 1.1	< 0.001
3. Lack of cooking skills for making tasty low-sugar snacks	2.7 ± 1.3	2.9 ± 1.3	2.5 ± 1.3	0.003
4. The size of beverages (sweetened coffee, carbonated drinks, etc.) is large	2.5 ± 1.3	2.8 ± 1.3	2.2 ± 1.1	< 0.001
5. Lack of information on places that sell low-sugar beverages/snacks	2.5 ± 1.3	2.6 ± 1.3	2.3 ± 1.3	0.011
6. There are many sugary beverages/snacks at home	2.6 ± 1.3	3.0 ± 1.3	2.3 ± 1.2	< 0.001
7. Sugary beverages/snacks taste good	3.2 ± 1.3	3.6 ± 1.2	2.9 ± 1.2	< 0.001
8. Sugary beverages/snacks are easily available	3.2 ± 1.3	3.5 ± 1.2	2.9 ± 1.3	< 0.001
9. Convenience (easy to prepare, carry and consume on the go)	3.1 ± 1.3	3.5 ± 1.2	2.7 ± 1.3	< 0.001
10. Sugary beverages/snacks are relatively inexpensive	2.7 ± 1.2	3.0 ± 1.2	2.5 ± 1.2	< 0.001
11. Lack of time for grocery shopping or cooking	2.8 ± 1.3	3.1 ± 1.3	2.6 ± 1.3	< 0.001
12. Exposure to advertisements for sugary beverages/snacks in the media	2.7 ± 1.3	3.0 ± 1.3	2.5 ± 1.2	< 0.001
How difficult or easy is it to refrain from consuming sugary beverages/snacks in the following situations?
13. When I feel anxious or stressed	2.8 ± 1.1	2.7 ± 1.1	2.9 ± 1.1	0.011
14. When others (family members, friends) consume sugary beverages/snacks	2.9 ± 1.2	2.7 ± 1.2	3.1 ± 1.2	< 0.001
15. When I choose beverages/snacks at cafes or restaurants	3.0 ± 1.2	2.7 ± 1.2	3.2 ± 1.2	< 0.001
Control beliefs about lack of knowledge and skills³⁾	14.8 ± 5.8	16.5 ± 6.1	13.4 ± 5.0	< 0.001
Control beliefs about facilitating factors of sugar intake⁴⁾	14.9 ± 5.2	16.6 ± 4.8	13.5 ± 5.2	< 0.001
Control beliefs about situations promoting sugar intake⁵⁾	12.0 ± 3.7	11.1 ± 3.8	12.7 ± 3.5	< 0.001
Total control beliefs score⁶⁾	48.2 ± 12.6	43.9 ± 12.6	51.8 ± 11.4	< 0.001

Variables	Stages of change					P-value¹⁾	>P for trend²⁾
Variables	Precontemplation (n = 39)	Contemplation (n = 72)	Preparation (n = 61)	Action (n = 96)	Maintenance (n = 112)	P-value¹⁾	>P for trend²⁾
Behavioral beliefs³⁾	41.8 ± 6.9	42.4 ± 6.9	42.3 ± 5.9	38.1 ± 7.1	36.5 ± 7.6	< 0.001	< 0.001
Mean of subjective norms⁴⁾	7.8 ± 3.9	11.3 ± 4.5	13.1 ± 5.3	10.8 ± 4.1	9.1 ± 5.0	< 0.001	0.275
Control beliefs⁵⁾	48.6 ± 14.9	40.5 ± 11.4	45.0 ± 11.5	49.1 ± 9.8	54.1 ± 12.1	< 0.001	< 0.001

Variables	Stages of change					P-value¹⁾	P for trend²⁾
Variables	Precontemplation (n = 39)	Contemplation (n = 72)	Preparation (n = 61)	Action (n = 96)	Maintenance (n = 112)	P-value¹⁾	P for trend²⁾
Beliefs regarding advantages of consuming sugary beverages/snacks³⁾	28.6 ± 7.7	31.6 ± 6.5	30.9 ± 6.6	27.6 ± 6.0	26.3 ± 7.0	< 0.001	0.001
Beliefs regarding disadvantages of consuming sugary beverages/snacks⁴⁾	22.7 ± 5.1	25.2 ± 3.7	24.5 ± 3.7	25.5 ± 3.0	25.8 ± 4.3	< 0.001	< 0.001
Mean of subjective norms⁵⁾	7.8 ± 3.9	11.3 ± 4.5	13.1 ± 5.3	10.8 ± 4.1	9.1 ± 5.0	< 0.001	0.275
Control beliefs about lack of knowledge and skills⁶⁾	14.8 ± 6.1	17.7 ± 6.1	16.2 ± 6.0	14.8 ± 4.6	12.2 ± 5.1	< 0.001	< 0.001
Control beliefs about facilitating factors of sugar intake⁷⁾	15.2 ± 5.8	17.8 ± 4.2	16.2 ± 4.6	14.5 ± 4.6	12.7 ± 5.5	< 0.001	< 0.001
Control beliefs about situations promoting sugar intake⁸⁾	12.6 ± 4.5	9.9 ± 3.6	11.4 ± 3.1	12.4 ± 3.4	13.0 ± 3.6	< 0.001	0.031

Table 1. General characteristics of participants by the stages of change in reducing sugar intake

n (%) or Mean ± SD.

By χ²-test or analysis of variance (ANOVA).

Table 2. Consumption frequency of sugary foods by the stages of change in reducing sugar intake

Mean ± SD.

By analysis of covariance (ANCOVA), adjusted for sex, age, BMI, and occupation.

The consumption frequency of each food item was measured using nine response categories from “never” to “more than three times per day.”

Summated consumption frequency of sugary foods in each food group per week.

Table 3. Behavioral beliefs regarding sugar intake of participants by the stages of change in reducing sugar intake

Mean ± SD.

By ANCOVA, adjusted for sex, age, BMI, and occupation.

Each item was measured by five-point scale from “strongly disagree” (1) to “strongly agree” (5).

Subscale score for nine items (Items 1–9), possible score: 9–45. Higher scores indicated stronger agreement with the advantages of consuming sugary beverages/snacks.

Subscale score for six items (Items 10–15), possible score: 6–30. Higher scores indicated stronger agreement with the disadvantages of consuming sugary beverages/snacks.

Total score for 15 items, possible score: 15–75. The items assessing disadvantages were scored in reverse. Higher scores indicated more favorable beliefs about consuming sugary beverages/snacks.

Table 4. Subjective norms regarding sugar intake of participants by the stages of change in reducing sugar intake

Mean ± SD.

By ANCOVA, adjusted for sex, age, BMI, and occupation.

Possible score per item: 1–25. Scores were calculated by multiplying each normative belief score (from “strongly disagree” (1) to “strongly agree” (5)) by corresponding motivation to comply score (from “not at all” (1) to “very much” (5)). Responses marked as “not applicable” were excluded from item-level calculations.

Number of participants included in the analysis for each item, excluding responses marked as “not applicable” for either normative belief or motivation to comply.

Possible score: 1–25. The mean subjective norms score reflect the overall influence of significant others, accounting for the differences in the number of applicable items across participants. For each participant, the sum of item scores was divided by the number of completed items, excluding “not applicable” referents from both numerator and denominator. Higher scores indicated greater perceived influence from significant others.

Table 5. Control beliefs regarding sugar intake of participants by the stages of change in reducing sugar intake

Mean ± SD.

By ANCOVA, adjusted for sex, age, BMI, and occupation.

Items were measured by five-point scales from “strongly disagree” (1) to “strongly agree” (5), or from “very difficult” (1) to “very easy” (5).

Subscale score for six items (Items 1–6), possible score: 6–30. Higher scores indicated greater agreement with insufficient knowledge and skills regarding sugar intake.

Subscale score for five items (Items 7–11), possible score: 5–25. Higher scores indicated lower control beliefs regarding factors facilitating sugar intake.

Subscale score for four items (Items 12–15), possible score: 4–20. Item 12 was scored in reverse to calculate the subscale score. Higher scores indicated stronger control beliefs in situations promoting sugar intake.

Total score for 15 items; possible score: 15–75. Items 1–12 were scored in reverse. Higher scores indicated stronger control beliefs regarding sugar intake.

Table 6. Factors related to the stages of change in reducing sugar intake at the total score level of variables

Mean ± SD.

By ANCOVA, adjusted for sex, age, BMI, and occupation.

By one-way ANOVA with linear contrast.

Total score for 15 items; possible score: 15–75. The items assessing disadvantages were scored in reverse. Higher scores indicated more favorable beliefs about consuming sugary beverages/snacks.

Total score for 15 items; possible score: 15–75. Items 1–12 were scored in reverse. Higher scores indicated stronger control beliefs regarding sugar intake.

Table 7. Factors related to the stages of change in reducing sugar intake at the subscale score level of variables

Mean ± SD.

By ANCOVA, adjusted for sex, age, BMI, and occupation.

By one-way ANOVA with linear contrast.

Subscale score for nine items, possible score: 9–45. Higher scores indicated stronger agreement with the advantages of consuming sugary beverages/snacks.

Subscale score for six items, possible score: 6–30. Higher scores indicated stronger agreement with the disadvantages of consuming sugary beverages/snacks.

Subscale score for six items, possible score: 6–30. Higher scores indicated greater agreement with insufficient knowledge and skills regarding sugar intake.

Subscale score for five items, possible score: 5–25. Higher scores indicated lower control beliefs regarding factors facilitating sugar intake.

Subscale score for four items, possible score: 4–20. Item 12 was scored in reverse to calculate the subscale score. Higher scores indicated stronger control beliefs in situations promoting sugar intake.