Healthy Behaviors and All-Cause Mortality among Korean Gastric Cancer Survivors

Donghyun Won; Jeeyoo Lee; Sooyoung Cho; Ji Yoon Baek; Hyuk-Joon Lee; Aesun Shin

doi:10.4143/crt.2025.177

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Original Article Healthy Behaviors and All-Cause Mortality among Korean Gastric Cancer Survivors: Donghyun Won^1,2, Jeeyoo Lee¹, Sooyoung Cho³, Ji Yoon Baek^1,2, Hyuk-Joon Lee^4,5, Aesun Shin^1,2,3,5,6; DOI: https://doi.org/10.4143/crt.2025.177
Published online: September 3, 2025

¹Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea

²Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea

³Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea

⁴Department of Surgery, Seoul National University College of Medicine, Seoul, Korea

⁵Cancer Research Institute, Seoul National University, Seoul, Korea

⁶Interdisciplinary Program in Cancer Biology Major, Seoul National University College of Medicine, Seoul, Korea

Correspondence: Aesun Shin, Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: 82-2-740-8331 E-mail: shinaesun@snu.ac.kr

• Received: February 14, 2025 • Accepted: September 1, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://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

Purpose
The incidence, prevalence, and survival rates of gastric cancer are high, and the prognostic effects of healthy behaviors among survivors have not been well investigated. Therefore, we aimed to assess the effects of postdiagnosis healthy behaviors and behavior changes after gastric cancer diagnosis on all-cause mortality.
Materials and Methods
As a population-based retrospective cohort study, we used a cancer public library sample DB of gastric cancer patients between 2012 and 2019. Information from regular health check-up examinations was used to investigate their anthropometric measures, physical activities, alcohol consumption, and smoking status before and after cancer diagnosis. Hazard ratios (HRs) for all-cause deaths with 95% confidence intervals (CIs) were estimated via the Cox proportional hazards model.
Results
We analyzed 9,717 gastric cancer patients and 5,929 of those as a subgroup to assess the effects of behavior changes. Reduced mortality was shown among cancer patients who met the recommended criteria after the cancer diagnosis for physical activity (adjusted HR, 0.67 [95% CI, 0.49 to 0.93], mean frequencies of moderate to vigorous physical activity per week: ≥ 5 days vs. 0 days) and smoking cessation (0.77 [0.61 to 0.97], smoking status: never-smokers vs. current smokers). Participants who reported enhancement in behaviors had significantly lower mortality than the others who reported no or limited changes in physical activity (0.73 [0.55 to 0.96]) and smoking status (0.56 [0.38 to 0.83]).
Conclusion
The current study highlights the advantages of physical activity and smoking cessation in reducing mortality, and these benefits are even greater when patients improve their behavior after cancer diagnosis.
Key words: Stomach neoplasms, Mortality, Survival, Health behavior, Republic of Korea

Introduction

Gastric cancer ranks fifth in both incidence and mortality globally, with an estimated 968,350 new cases and 659,853 gastric cancer–specific deaths reported in 2022 [1]. In South Korea, gastric cancer is the fourth most common disease and the second most prevalent cancer. The National Cancer Screening Program for gastric cancer in South Korea has made significant improvements in survival rates [2], providing a unique opportunity to address the prognostic role of improving healthy behaviors among gastric cancer survivors.

One of the most common risk factors for gastric cancer is infection with Helicobacter pylori [3]. Obesity, low fruit consumption, high intake of preserved foods or sodium, and smoking are modifiable factors that increase the risk of gastric cancer [4]. The World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) provided evidence-based cancer prevention recommendations in 2018, giving healthy behavior guidelines for modifiable cancer risk factors [5]. However, the impact of these behaviors on mortality among gastric cancer survivors is still debated, and evidence of their prognostic effects is needed [6]. A thorough investigation is needed to evaluate the prognostic effects of modifiable behavior improvements, since cancer patients are highly vulnerable due to their clinical condition, experience rapid weight loss (depending on postoperative results), and may suffer from decreased physical function [7]. For example, some research has shown that a high body mass index (BMI) has a negative impact on cancer risk, though a previous study reported that a high BMI might have a positive impact on cancer patients during surgery [8]. A previous cohort study in Korea reported a better prognosis among gastric cancer patients who were preoperatively overweight or obese than among those with normal weight [9]; however, the effects of postdiagnosis body weight on survival were not investigated. The effects of postdiagnosis physical activity on all-cause mortality also depend on the clinical status of patients, so the results vary across studies [10]. The impact of postdiagnosis dietary habits or alcohol consumption among gastric cancer survivors also differs according to the indices or criteria applied [11].

Therefore, the current study aimed to comprehensively assess the prognostic effects of healthy behaviors, including maintaining a healthy weight, being physically active, limiting alcohol consumption, and quitting smoking, on all-cause mortality. In this large nationwide retrospective cohort of gastric cancer survivors, we examined the effects of both postdiagnosis healthy behaviors and behavior changes toward healthier behaviors on all-cause mortality.

Materials and Methods

The Cancer Public Library Database is a longitudinal nationwide dataset which provides a randomly selected 10% sample of the total number of registered gastric cancer survivors. The database consists of four major population-based public sources in Korea, using this dataset ensures that the current study uses a reliable representation of registered cancer survivors in Korea. It was established under the Korean Clinical Utilization for Research Excellence project, comprising the Korea National Cancer Incidence Database in the Korea Central Cancer Registry (2012-2019), cause-of-death data in Statistics Korea (2012-2020), the National Health Information Database in the National Health Insurance Service (2012-2021), and the National Health Insurance Research Database in the Health Insurance Review & Assessment Service (2012-2021) [12]. We used regular health check-up examination data from the National Health Information Database to evaluate behavior. For participants who underwent multiple health check-up examinations, we selected only the data closest to the date of cancer diagnosis. Health check-up examination data included height, weight, and waist circumference. These variables were measured by trained medical staff, with units of 10 cm or 10 kg for each. Accordingly, we used the calculated BMI provided by the database due to the limited availability of alternative data. Self-reported behaviors are also included in the database and include: frequency of moderate physical activity per week, frequency of vigorous physical activity per week, amount of alcohol consumption, and smoking status.

Individuals with International Classification of Diseases for Oncology codes, 3rd edition of C160-C166 or C168-C169, were registered in the Korea Central Cancer Registry as patients with gastric cancer. The database included a randomly selected sample of 10% of the total registered gastric cancer patients between 2012 and 2019 (n=23,717) [12]. We excluded the cases where the year of cancer diagnosis and the year of the health check-up are the same (n=2,271), as only the year of health check-up examinations and the years and months of cancer diagnosis or death were provided which disables the distinction between pre- and post-diagnostic behaviors among the cases when a health check-up and cancer diagnosis both occurred in the same year. Study population who did not complete examinations after their cancer diagnosis (n=9,166) and who completed health check-up examinations after January 2021, when the mortality follow-up ended (n=2,543) was excluded. Additionally, patients who did not provide information on their postdiagnosis behaviors (n=20) were excluded. A total of 9,717 patients were included in the study, of whom 5,929 were analyzed for the impact of behavioral changes on mortality (Fig. 1).

We comprehensively evaluated four categories of healthy behaviors, including maintaining a healthy weight, being physically active, limiting alcohol consumption, and quitting smoking. We then divided the participants into three groups: poor, moderate, and good. Ratings were made on the basis of the degree to which they met the WCRF/AICR criteria [5].

We modified the criteria to better align with the South Korean context, as shown in S1 Table. First, BMI and waist circumference were used to determine whether the participants adhered to the healthy weight criteria. Data were extracted with units of 1 kg/m², and by 10 cm from the database. We adapted the criteria of the Korean Society for the Study of Obesity [13]. We initially assigned points for each BMI (zero points for BMIs [kg/m²] less than 19.0 or more than 25.0, 0.25 points for BMIs [kg/m²] between 23.0 and 25.0, and 0.50 points for BMIs [kg/m²] between 19.0 and 23.0) and waist circumference (zero points for waist circumferences ≥ 90 cm [men] or ≥ 80 cm [women], and 0.50 points for waist circumferences < 90 cm [men] or < 80 cm [women]). We then added the points and divided the participants into three groups according to adherence: participants who received a score of 1.00 point were considered good, participants with scores between 0.50 and 0.75 points were considered moderate, and participants with scores less than 0.25 points were considered poor. Second, adherence of physical activity was evaluated by measuring the mean frequencies of moderate and vigorous physical activity (days/week); participants who reported more than 5 days were considered good, participants who reported between 1-4 days were considered moderate, and participants who reported 0 days were considered poor. The criteria were modified according to the Korean National Cancer Center because of the unavailability of physical activity time data [14]. Third, we analyzed data regarding total daily alcohol intake and defined nondrinkers as good, light drinkers (participants who drank less than 2 drinks (men) or 1 drink (women)) as moderate, and heavy drinkers (participants who drank more than light drinkers) as poor. The reference volume was determined on the basis of the low-risk drinking guidelines in Korea for each alcohol type: 50 mL (one shot glass) of soju, 250 mL (one glass) of beer, 35 mL (one shot glass) of liquor, 150 mL (one shot glass) of Korean liquor, and 125 mL (one glass) of wine. When participants responded that they drank more than once in the past year but did not indicate their amount of drinking, we imputed the median drinking amount by sex. As the questionnaire concerning alcohol consumption and smoking habits has changed since 2018, we adjusted it in a consistent manner or included only common items that were available both before and after the change. Regarding smoking habits, we divided participants into three groups on the basis of cigarette smoking status. People who never smoked were classified as good, former smokers were classified as moderate, and current smokers were categorized as poor.

Additional data that were extracted included information on the patients’ cancer diagnosis, socioeconomic status, and history of other diseases. Age, sex, and cancer stage data were collected at the time of cancer diagnosis from the Korea National Cancer Incidence Database. We considered cancer stages I to IV or unknown, which were derived from the 7th edition of the American Joint Committee on Cancer (AJCC) [15]. Socioeconomic status was categorized on the basis of status in December of the year of cancer diagnosis. Sub-domains of this variable included insurance type, income level, and residence. We classified insurance types as locally insured, employee insured, medical-aid beneficiary or missing. We then divided insurance deciles into tertiles according to income level. Residence was provided in three categories: Seoul; Metropolitan cities, including Busan, Daegu, Incheon, Gwangju, Daejeon, Ulsan, and Sejong; and other regions, including Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju. Disease history was collected from the results of postdiagnostic health check-up examinations on the basis of the National Health Information Database. We defined patients with hypertension as those whose systolic blood pressure was ≥ 140 mmHg, diastolic blood pressure was ≥ 90 mmHg, or who had a history of hypertension. Patients with diabetes were also defined as those who had ≥ 126 mg/dL fasting blood glucose or those who reported a history of diabetes.

Mortality follow-up data regarding cause of death, which were available until December 31, 2020, were extracted from Statistics Korea. For each participant, the follow-up period was from the date of the postdiagnosis during health check-up examinations to the time of the study. Additionally, we set the starting date as the date of cancer diagnosis for the analysis assessing the impact of behavior changes on all-cause mortality. The follow-up period subsequently ended with the date of death or the end of the cohort study.

The distribution of demographic variables was summarized by the number and percentages of the participants for each variable. The associations between healthy behaviors and all-cause mortality were assessed via the Cox proportional hazard model, and hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated. Participants with poor behaviors after their cancer diagnosis and those in the poor behavior group were used as a reference for each evaluation. We adjusted for patient age at diagnosis, sex, number of years postdiagnosis, cancer stage, insurance type, income level, and residence, following previous studies [16]. Subgroup analysis was conducted according to sex and cancer stage. A two-sided p-value of < 0.05 was considered statistically significant for all the statistical analyses. Data analysis was conducted via the SAS statistical software package ver. 9.4 (SAS Institute Inc.).

Results

The baseline characteristics of the gastric cancer patients are shown in Table 1. A total of 69.1% (n=6,716) of the participants were men, and 84.6% (n=8,223) of the participants had been diagnosed with stage I gastric cancer. Figs. 2 and 3 in the Supplement provide a summary of the changes in behavior among the study population by sex. Over 40% (n=1,917) of the men participants maintained a consistently normal weight, whereas another 40% (n=1,738) experienced variation in their weight, sometimes being closer to and sometimes being further from a normal weight. Among the women participants, 22.3% (n=669) experienced changes in body weight. For physical activity, participants showed patterns of both changes and maintenance. Among participants who reported changes, more participants reported increases in physical activity; this was the case with 17.0% (n=1,140) of men participants and 17.5% (n=525) of women participants. In terms of alcohol consumption and smoking habits, women participants consistently reported healthy behaviors, whereas men participants improved their healthy behaviors following their diagnosis.

Table 2 shows the associations between postdiagnosis behaviors and all-cause mortality during a mean follow-up of 0.6 years (standard deviation [SD], 0.5). We observed that either being physically active or quitting smoking was related to decreased mortality. Compared with the poor-adherence group, reduced mortality was observed in the good-adherence group (adjusted HR [aHR], 0.67 [95% CI, 0.49 to 0.93]; more than 5 days of physical activity) and the moderate-adherence group (aHR, 0.70 [95% CI, 0.60 to 0.82]; between 1-4 days of physical activity). Compared with current smokers, former smokers (aHR, 0.62 [95% CI, 0.49 to 0.78]) and never smokers (aHR, 0.77 [95% CI, 0.61 to 0.97]) had significantly lower mortality. Light drinkers had a lower mortality (aHR, 0.72 [95% CI, 0.52 to 1.00]) than heavy drinkers. Regarding healthy weight, the good-adherence group (BMI between 19.0 kg/m² and 23.0 kg/m²; waist circumference less than 90 cm for men or less than 80 cm for women) had significantly greater mortality (aHR, 1.28 [95% CI, 1.03 to 1.59]) than the poor-adherence group (BMI less than 19.0 kg/m² or greater than 23.0 kg/m²; waist circumference greater than 90 cm in men or greater than 80 cm in women). Decreased mortality was found among men in the moderate- and good-adherence groups for physical activity and smoking habits compared to the poor-adherence groups. These relationships were significant only for smoking habits among women. S2 Table shows the results of subgroup analysis by cancer stage. Lowered mortality was shown among those who met the criteria for physical activity, smoking habit, or alcohol consumption. However, significant reductions in all-cause mortality were shown only among participants who were diagnosed with stage I cancer, mainly because most participants in our study were diagnosed with stage I cancer (n=8,223).

Table 3 shows the effects of behavior changes on mortality during a mean follow-up of 1.2 years (SD, 0.5). A significant reduction in all-cause mortality was observed among those who reported improvements in healthy behavior in terms of physical activity and smoking habits. Reduced mortality was found among either participants who reported more frequent physical activity (aHR, 0.73; 95% CI, 0.55 to 0.96) or who maintained frequent physical activity (aHR, 0.46; 95% CI, 0.33 to 0.64) than among those who maintained physically inactive. All-cause mortality was also significantly lower in patients who reported postdiagnostic smoking cessation (aHR, 0.56; 95% CI, 0.38 to 0.83) or never smoking (aHR, 0.60; 95% CI, 0.41 to 0.88) than in those who reported to smoke both before and after cancer diagnosis. S3 Table shows that men participants who previously consumed less alcohol but whose alcohol consumption increased after cancer diagnosis had lower mortality (aHR, 0.40; 95% CI, 0.19 to 0.84) than did men participants who had poor behavior regarding alcohol consumption both before and after cancer diagnosis. However, women who reported increases (aHR, 0.11; 95% CI, 0.02 to 0.61) or maintenance of behaviors (aHR, 0.14; 95% CI, 0.03 to 0.63) related to alcohol consumption had improved prognoses. S4 Table provides the results of subgroup analysis by cancer stage at diagnosis. Significant decreases in mortality were observed among those who strongly adhered to the criteria for physical activity (aHR, 0.53; 95% CI, 0.35 to 0.81), alcohol consumption (aHR, 0.51; 95% CI, 0.31 to 0.84), or smoking habits (aHR, 0.28; 95% CI, 0.18 to 0.44). This was especially the case among those who were diagnosed with stage 1 gastric cancer. Reduced mortality was also observed in those who improved their physical activity (aHR, 0.63; 95% CI, 0.43 to 0.90) or smoking habits (aHR, 0.48; 95% CI, 0.30-0.77) compared with the participants who maintained poor behaviors.

In addition to combining BMI and waist circumference as a single predictor, we examined BMI to assess its change after gastric cancer diagnosis and its impact on all-cause mortality. Compared with those with a postdiagnosis BMI less than 21.0 kg/m², individuals with a postdiagnosis BMI between 27.0 kg/m² and 30.0 kg/m² had the lowest mortality (aHR, 0.45 [95% CI, 0.31 to 0.66] among men; aHR, 0.37 [95% CI, 0.18 to 0.77] among women), as shown in S5 Fig. in the Supplement. Those with a BMI between 21.0 kg/m² and 29.0 kg/m² had significantly lower mortality than those with a BMI under 21.0 kg/m². S6 Fig. shows that an increased BMI after diagnosis was also associated with reduced mortality, although the results had wide 95% CIs.

Discussion

We observed a strong association between physical activity or smoking cessation and all-cause mortality among gastric cancer survivors. Our findings indicate that both postdiagnosis healthy behaviors and enhanced healthy behaviors after a gastric cancer diagnosis may contribute to improved survival. This association was especially prominent among participants who were diagnosed with gastric cancer at an early stage. Compared with participants who maintained poor behavior, those who maintained a high frequency of physical activity before and after cancer diagnosis or who quit smoking after cancer diagnosis presented the lowest mortality.

Our results are consistent with those of previous studies from East Asia. In China, weight loss and a smoking history of more than 30 years were associated with a poor prognosis but were not associated with alcohol consumption [17]. Another previous study in Japan showed a poor prognosis among those who were underweight, alcohol drinkers, or smokers, and a good prognosis among those who exercised [18]. A cohort study in South Korea found that behavior changes toward healthier lifestyle reduced all-cause mortality among total cancer survivors, but among gastric cancer survivors, only smoking status was significantly associated with a decrease in mortality [19].

By including greater number of gastric cancer survivors, however, the current study found an association physical activity and reduced all-cause mortality, consistent with previous in vivo studies that showing physical activity’s impact on tumor growth and progression [20]. Physical activity is also known to help improve delivery of anticancer therapies by reducing tumor hypoxia and increasing blood flow [21]. Postdiagnosis smoking might directly induce the formation of metastases, resulting in side effects or treatment resistance [22]. Smoking is associated with comorbidities, low treatment adherence, and other risky behaviors, which may indirectly result in increased mortality [23]. The fact that unhealthy behaviors are related to one another might explain the finding that the lowest mortality was observed among participants who quit smoking after their cancer diagnosis; their mortality was lower even than that of participants who were consistently never-smokers.

The effects of unintentional weight loss on all-cause mortality have also been highlighted in previous studies [24]. Gastric cancer survivors with increased weight are sometimes reported to survive longer, since weight loss resulting from gastrointestinal tract obstruction, anorexia, malabsorption, or hypermetabolism results in a poor prognosis [25]. However, patients with high BMIs are also reported to be more at risk of delayed wound healing and infections due to insulin resistance and poor glycemic control [26]. The paradoxical protective effects of obesity found in the current and previous studies were suggested to be found only when obesity was defined by BMI, highlighting a need for additional research using alternative measures, such as muscle tissue mass, visceral fat mass, and subcutaneous fat mass [27].

Although our study results suggest that increased alcohol consumption after a cancer diagnosis may be associated with lower mortality, caution is needed when advising gastric cancer patients to consume alcohol after diagnosis, as pre-cancer characteristics such as age at drinking initiation or drinking patterns may have influenced the effects of alcohol consumption. Given that gastric cancer occurs more frequently in men [4] and that men tend to start drinking at an earlier age and engage in alcohol consumption more frequently than women [28], the lower mortality observed in the ‘worsen’ group may be explained by the fact that this group includes individuals who started drinking only after their diagnosis, resulting in a shorter overall period of alcohol consumption. Since even light alcohol consumption, if maintained over a long period, can increase the incidence and mortality rates of gastric cancer [29-31], a more detailed analysis of the association between frequency or patterns of alcohol use by sex and mortality might better provide evidence on the sex-specific thresholds used in the current study.

As a large-scale retrospective cohort study, the current study included participants from a nationally representative database, making the data highly reliable and accurate. The population of the current study included a random selection of 10% of all registered cancer patients, which guaranteed both study power and generalizability. Previous studies using existing claims databases had the possibility of miscoding, since disease diagnoses might not accurately reflect patients’ medical conditions [32]. The use of a cancer registry database resulted in no misclassification of cancer diagnoses. We also considered detailed information on disease progression with American Joint Committee on Cancer, 7th edition staging information [15]. By employing cancer stage at diagnosis for adjustment or subgroup analysis, we might have minimized the confounding effects of cancer stage on all-cause mortality among gastric cancer survivors.

Despite the strengths of this study, some limitations must be considered. First, the relatively short follow-up time might be insufficient for interpreting the effect sizes of the associations between postdiagnosis behaviors and all-cause mortality. Second, the database that was used presents a potential data granularity problem, where some details are omitted from the results. For example, the dates of health check-up examinations were provided annually, and the dates of cancer diagnosis or death were provided monthly. In our study, we defined months for health check-up examinations as January for all participants for the main results. Nonetheless, these findings might be interpreted as not fully representative of the target population, since we did not find differences when performing sensitivity analyses that verified postdiagnosis health check-up examinations in June and December. Body weight data were also omitted, since BMI and waist circumference data were available with units of 1 kg/m² and 10 cm, respectively. Further research using detailed BMI and waist circumference data is needed for a more accurate estimation of the association between postdiagnostic body weight and mortality. In addition, the category of residence could not fully represent the real-world status in South Korea, as Gyeonggi may have similar characteristics to “metropolitan cities” rather than “other regions.” This is one of the limitations of the database, which only provides categorized groups of residence. Third, current study cannot inform the intention behind the weight changes, making it challenging to distinguish between the “unintentional weight loss” and “weight loss due to enhanced health behavior”, and to suggest an optimal weight. Unintentional weight loss causes significant prognostic harm for patients with gastric cancer, highlighting the need for interventions focused on weight management and future studies to define the intention behind weight changes [33]. Finally, the data may be influenced by response and recall bias, as we used the self-reported health behaviors as exposure variables. Bias is especially common in sensitive questions, such as those related to body weight and mental health [34]. To address this limitation, we used BMI and waist circumference that were measured by trained medical staff. Comparing the results at two time points (pre- and post-diagnosis) might also help mitigate bias within each individual.

The current study consisted of a large gastric cancer patient cohort demonstrate that smoking cessation and maintenance of frequent physical activity after cancer diagnosis can improve survival rates for gastric cancer survivors.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2025-177_S1_Table.pdf

crt-2025-177_S2_Table.pdf

crt-2025-177_S3_Table.pdf

crt-2025-177_S4_Table.pdf

crt-2025-177_S5_Fig.pdf

crt-2025-177_S6_Fig.pdf

NOTES

Ethical Statement

The study was approved by the Institutional Review Board of Institutional Review Boards of Seoul National University Hospital (IRB no. E-2312-132-1496), and informed consent for participation was obtained from all subjects involved in the study.

Author Contributions

Conceived and designed the analysis: Won D, Lee J, Cho S, Shin A.

Collected the data: Won D.

Contributed data or analysis tools: Won D, Lee J, Cho S.

Performed the analysis: Won D.

Wrote the paper: Won D.

Administered the project: Won D.

Reviewed and edited the manuscript: Lee J, Cho S, Baek JY, Lee HJ, Shin A.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2C1004608).

Acknowledgments

We declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

The author (A.S.) has full access to all the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis. The data could be accessed by applying to the Korea National Cancer Center (https://www.cancerdata.re.kr/).

Fig. 1.

Flowchart of the study participants with gastric cancer (2012-2019).

Fig. 2.

Changing patterns of behaviors among men participants. Behavior changes were compared from pre to postdiagnosis.

Fig. 3.

Changing patterns of behaviors among women participants. Behavior changes were compared from pre to postdiagnosis.

Table 1.

Baseline characteristics of gastric cancer patients at diagnosis from Cancer Public Library Database (2012-2019)

	Men (n=6,716)	Women (n=3,001)
Age at diagnosis (yr)
< 40	119 (1.8)	138 (4.6)
40-49	633 (9.4)	429 (14.3)
50-59	1,901 (28.3)	709 (23.6)
60-69	2,304 (34.3)	853 (28.4)
70-79	1,539 (22.9)	751 (25.0)
≥ 80	220 (3.3)	121 (4.0)
Cancer stage (AJCC-7)
I	5,677 (84.5)	2,546 (84.8)
II	508 (7.6)	250 (8.3)
III	408 (6.1)	160 (5.3)
IV	65 (1.0)	20 (0.7)
Unknown	58 (0.9)	25 (0.8)
Years since diagnosis	0.8±0.5	0.8±0.5
Insurance type
Local insured	1,797 (26.8)	828 (27.6)
Employee insured	4,800 (71.5)	2,059 (68.6)
Medical-aid beneficiary	119 (1.8)	114 (3.8)
Income level (decile)
Lowest (tertile 1: 0-3)	1,464 (21.8)	843 (28.1)
Middle (tertile 2: 4-7)	2,238 (33.3)	923 (30.8)
Highest (tertile 3: 8-10)	2,870 (42.7)	1,168 (38.9)
Missing	144 (2.1)	67 (2.2)
Residence^a)
Seoul	988 (14.7)	425 (14.2)
Metropolitan cities	2,054 (30.6)	917 (30.6)
Others	3,674 (54.7)	1,659 (55.3)
History of hypertension
No	3,911 (58.2)	1,830 (61.0)
Yes^b)	2,793 (41.6)	1,161 (38.7)
Missing	12 (0.2)	10 (0.3)
History of diabetes
No	5,135 (76.5)	2,464 (82.1)
Yes^c)	1,566 (23.3)	520 (17.3)
Missing	15 (0.2)	17 (0.6)

Values are presented as number (%) or mean±SD. AJCC-7, American Joint Committee on Cancer, 7th edition; n, the number of corresponding cases.

^a) Metropolitan cities include Busan, Daegu, Incheon, Gwangju, Daejeon, Ulsan, Sejong in Korea. Other regions include Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju in Korea,

^b) Defined as those who were measured as having ≥ 140 mmHg of systolic blood pressure or ≥ 90 of diastolic blood pressure, or those who self-reported hypertension,

^c) Defined as those who were measured as having ≥ 126 of fasting blood glucose, or those who self-reported the history of diabetes.

Table 2.

Cox proportional hazard models demonstrating association between postdiagnosis healthy behaviors and all-cause mortality, stratified by sex^a)

	Overall (n=9,717)			Men (n=6,716)			Women (n=3,001)
	Poor	Moderate	Good	Poor	Moderate	Good	Poor	Moderate	Good
Be a healthy weight^b)
No.	2,069	2,573	5,075	1,224	2,059	3,433	845	514	1,642
No. of deaths	108	175	473	78	149	388	30	26	85
Person-years	1,260.3	1,604.0	3,313.7	748.0	1,288.8	2,255.5	512.3	315.3	1,058.2
HR (95% CI)	Ref.	1.27 (1.00-1.62)	1.67 (1.36-2.06)	Ref.	1.11 (0.84-1.46)	1.65 (1.30-2.11)	Ref.	1.41 (0.83-2.38)	1.39 (0.92-2.11)
aHR (95% CI)	Ref.	1.17 (0.92-1.50)	1.28 (1.03-1.59)	Ref.	1.12 (0.85-1.48)	1.24 (0.97-1.60)	Ref.	1.32 (0.77-2.27)	1.35 (0.87-2.09)
Be physically active^c)
No.	4,014	5,095	608	2,572	3,673	471	1,442	1,422	137
No. of deaths	455	259	42	361	217	37	94	42	5
Person-years	2,788.5	2,945.0	444.5	1,791.4	2,155.4	345.4	997.1	789.6	99.1
HR (95% CI)	Ref.	0.53 (0.46-0.62)	0.58 (0.43-0.80)	Ref.	0.50 (0.42-0.59)	0.54 (0.38-0.75)	Ref.	0.55 (0.38-0.79)	0.54 (0.22-1.33)
aHR (95% CI)	Ref.	0.70 (0.60-0.82)	0.67 (0.49-0.93)	Ref.	0.68 (0.57-0.81)	0.65 (0.46-0.91)	Ref.	0.75 (0.52-1.10)	0.89 (0.36-2.21)
Limit alcohol consumption^d)
No.	936	2,052	6,729	884	1,716	4,116	52	336	2,613
No. of deaths	65	83	608	63	79	473	2	4	135
Person-years	538.3	1,116.3	4,523.3	512.6	973.7	2,806.0	25.8	142.7	1,717.3
HR (95% CI)	Ref.	0.61 (0.44-0.85)	1.13 (0.87-1.45)	Ref.	0.66 (0.47-0.92)	1.39 (1.07-1.80)	Ref.	0.35 (0.06-1.90)	1.03 (0.26-4.18)
aHR (95% CI)	Ref.	0.72 (0.52-1.00)	0.96 (0.74-1.24)	Ref.	0.72 (0.51-1.00)	0.98 (0.75-1.28)	Ref.	0.52 (0.09-2.91)	0.67 (0.16-2.77)
Quit smoking^e)
No.	769	3,818	5,130	747	3,719	2,250	22	99	2,880
No. of deaths	106	258	392	102	256	257	4	2	135
Person-years	640.2	2,319.3	3,218.6	625.0	2,268.7	1,398.6	15.2	50.6	1,820.0
HR (95% CI)	Ref.	0.66 (0.53-0.83)	0.73 (0.59-0.90)	Ref.	0.68 (0.54-0.86)	1.11 (0.88-1.40)	Ref.	0.15 (0.03-0.83)	0.29 (0.11-0.78)
aHR (95% CI)	Ref.	0.62 (0.49-0.78)	0.77 (0.61-0.97)	Ref.	0.63 (0.50-0.80)	0.79 (0.62-1.00)	Ref.	0.18 (0.03-0.99)	0.26 (0.09-0.72)

aHR, adjusted hazard ratios; CI, confidence interval; HR, hazard ratio; Ref, reference.

^a) The overall model was adjusted for age, sex (men or women), year postdiagnosis, cancer stage (I, II, III, IV or unknown), insurance type (local insured, employee insured, medical-aid beneficiary or missing), income level (decile to tertile; the lowest (tertile 1: 0-3), middle (tertile 2: 4-7), and the highest (tertile 3: 8-10) or missing), and residence (Seoul, metropolitan cities or others). Sex was not adjusted in the sex-stratified models,

^b) Poor for BMI (kg/m²) < 19.0 or BMI (kg/m²) ≥ 23.0 and waist circumference ≥ 90 cm (men) or ≥ 80 cm (women), moderate for BMI (kg/m²) < 19.0 or BMI (kg/m²) ≥ 23.0 and waist circumference < 90 cm (men) or < 80 cm (women), and good for BMI (kg/m²) between 19.0 and 23.0 with waist circumference < 90 cm (men) or < 80 cm (women),

^c) Based on the mean frequencies of moderate to vigorous physical activity per week, poor for 0 day, moderate for 1-4 days, and good for ≥ 5 days,

^d) Based on the total alcohol consumption (drink/day), poor for heavy drinkers (men: ≥ 2, women: ≥ 1), moderate for light drinkers (men: < 2, women: < 1), and good point for non-drinkers,

^e) Based on the smoking status, poor for current smokers, moderate for former smokers, and good for non-smokers.

Table 3.

Cox proportional hazard models demonstrating association between behavior changes and all-cause mortality^a)

Behavior changes (Pre-Post)	Subset of the participants who had screened both before and after the diagnosis (n=5,929)
Behavior changes (Pre-Post)	Persistent poor (poor-poor)	Worsen (moderate-poor, good-poor, good-moderate)	Enhanced (poor-moderate, poor-good, moderate-good)	Persistent moderate/good (moderate-moderate, good-good)
Be a healthy weight
No.	951	597	1,840	2,541
No. of deaths	40	26	126	155
Person-years	1,109.5	697.0	2,208.8	3,039.3
HR (95% CI)	Ref.	1.03 (0.63-1.69)	1.57 (1.10-2.23)	1.40 (0.99-1.98)
aHR (95% CI)	Ref.	0.91 (0.55-1.49)	1.15 (0.80-1.67)	1.14 (0.80-1.63)
Be physically active
No.	1,388	1,054	1,665	1,822
No. of deaths	123	82	90	52
Person-years	1,657.7	1,283.2	1,949.4	2,164.3
HR (95% CI)	Ref.	0.86 (0.65-1.13)	0.63 (0.48-0.82)	0.33 (0.24-0.45)
aHR (95% CI)	Ref.	0.94 (0.71-1.25)	0.73 (0.55-0.96)	0.46 (0.33-0.64)
Limit alcohol consumption
No.	416	484	1,791	3,238
No. of deaths	23	10	108	206
Person-years	482.1	543.7	2,192.3	3,836.5
HR (95% CI)	Ref.	0.39 (0.19-0.82)	1.01 (0.65-1.59)	1.12 (0.73-1.72)
aHR (95% CI)	Ref.	0.35 (0.16-0.73)	0.91 (0.58-1.44)	1.03 (0.66-1.61)
Quit smoking^b)
No.	355	408	1,607	3,559
No. of deaths	35	30	97	185
Person-years	437.7	476.0	1,918.3	4,222.6
HR (95% CI)	Ref.	0.80 (0.49-1.31)	0.64 (0.43-0.94)	0.56 (0.39-0.80)
aHR (95% CI)	Ref.	0.61 (0.37-1.01)	0.56 (0.38-0.83)	0.60 (0.41-0.88)

aHR, adjusted hazard ratios; CI, confidence interval; HR, hazard ratio; Ref, reference.

^a) The model was adjusted for age, sex (men or women), year postdiagnosis, cancer stage (I, II, III, IV or unknown), insurance type (local insured, employee insured, medical-aid beneficiary or missing), income level (decile to tertile; the lowest (tertile 1: 0-3), middle (tertile 2: 4-7), and the highest (tertile 3: 8-10) or missing), and residence (Seoul, metropolitan cities or others),

^b) In the ‘quit smoking’, “Enhanced” only includes poor-moderate, as poor-moderate and moderate-good are logically impossible.

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Healthy Behaviors and All-Cause Mortality among Korean Gastric Cancer Survivors

DOI: https://doi.org/10.4143/crt.2025.177

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Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

Healthy Behaviors and All-Cause Mortality among Korean Gastric Cancer Survivors

Fig. 1. Flowchart of the study participants with gastric cancer (2012-2019).

Fig. 2. Changing patterns of behaviors among men participants. Behavior changes were compared from pre to postdiagnosis.

Fig. 3. Changing patterns of behaviors among women participants. Behavior changes were compared from pre to postdiagnosis.

Fig. 1.

Fig. 2.

Fig. 3.

Healthy Behaviors and All-Cause Mortality among Korean Gastric Cancer Survivors

	Men (n=6,716)	Women (n=3,001)
Age at diagnosis (yr)
< 40	119 (1.8)	138 (4.6)
40-49	633 (9.4)	429 (14.3)
50-59	1,901 (28.3)	709 (23.6)
60-69	2,304 (34.3)	853 (28.4)
70-79	1,539 (22.9)	751 (25.0)
≥ 80	220 (3.3)	121 (4.0)
Cancer stage (AJCC-7)
I	5,677 (84.5)	2,546 (84.8)
II	508 (7.6)	250 (8.3)
III	408 (6.1)	160 (5.3)
IV	65 (1.0)	20 (0.7)
Unknown	58 (0.9)	25 (0.8)
Years since diagnosis	0.8±0.5	0.8±0.5
Insurance type
Local insured	1,797 (26.8)	828 (27.6)
Employee insured	4,800 (71.5)	2,059 (68.6)
Medical-aid beneficiary	119 (1.8)	114 (3.8)
Income level (decile)
Lowest (tertile 1: 0-3)	1,464 (21.8)	843 (28.1)
Middle (tertile 2: 4-7)	2,238 (33.3)	923 (30.8)
Highest (tertile 3: 8-10)	2,870 (42.7)	1,168 (38.9)
Missing	144 (2.1)	67 (2.2)
Residence^a)
Seoul	988 (14.7)	425 (14.2)
Metropolitan cities	2,054 (30.6)	917 (30.6)
Others	3,674 (54.7)	1,659 (55.3)
History of hypertension
No	3,911 (58.2)	1,830 (61.0)
Yes^b)	2,793 (41.6)	1,161 (38.7)
Missing	12 (0.2)	10 (0.3)
History of diabetes
No	5,135 (76.5)	2,464 (82.1)
Yes^c)	1,566 (23.3)	520 (17.3)
Missing	15 (0.2)	17 (0.6)

	Overall (n=9,717)			Men (n=6,716)			Women (n=3,001)
	Poor	Moderate	Good	Poor	Moderate	Good	Poor	Moderate	Good
Be a healthy weight^b)
No.	2,069	2,573	5,075	1,224	2,059	3,433	845	514	1,642
No. of deaths	108	175	473	78	149	388	30	26	85
Person-years	1,260.3	1,604.0	3,313.7	748.0	1,288.8	2,255.5	512.3	315.3	1,058.2
HR (95% CI)	Ref.	1.27 (1.00-1.62)	1.67 (1.36-2.06)	Ref.	1.11 (0.84-1.46)	1.65 (1.30-2.11)	Ref.	1.41 (0.83-2.38)	1.39 (0.92-2.11)
aHR (95% CI)	Ref.	1.17 (0.92-1.50)	1.28 (1.03-1.59)	Ref.	1.12 (0.85-1.48)	1.24 (0.97-1.60)	Ref.	1.32 (0.77-2.27)	1.35 (0.87-2.09)
Be physically active^c)
No.	4,014	5,095	608	2,572	3,673	471	1,442	1,422	137
No. of deaths	455	259	42	361	217	37	94	42	5
Person-years	2,788.5	2,945.0	444.5	1,791.4	2,155.4	345.4	997.1	789.6	99.1
HR (95% CI)	Ref.	0.53 (0.46-0.62)	0.58 (0.43-0.80)	Ref.	0.50 (0.42-0.59)	0.54 (0.38-0.75)	Ref.	0.55 (0.38-0.79)	0.54 (0.22-1.33)
aHR (95% CI)	Ref.	0.70 (0.60-0.82)	0.67 (0.49-0.93)	Ref.	0.68 (0.57-0.81)	0.65 (0.46-0.91)	Ref.	0.75 (0.52-1.10)	0.89 (0.36-2.21)
Limit alcohol consumption^d)
No.	936	2,052	6,729	884	1,716	4,116	52	336	2,613
No. of deaths	65	83	608	63	79	473	2	4	135
Person-years	538.3	1,116.3	4,523.3	512.6	973.7	2,806.0	25.8	142.7	1,717.3
HR (95% CI)	Ref.	0.61 (0.44-0.85)	1.13 (0.87-1.45)	Ref.	0.66 (0.47-0.92)	1.39 (1.07-1.80)	Ref.	0.35 (0.06-1.90)	1.03 (0.26-4.18)
aHR (95% CI)	Ref.	0.72 (0.52-1.00)	0.96 (0.74-1.24)	Ref.	0.72 (0.51-1.00)	0.98 (0.75-1.28)	Ref.	0.52 (0.09-2.91)	0.67 (0.16-2.77)
Quit smoking^e)
No.	769	3,818	5,130	747	3,719	2,250	22	99	2,880
No. of deaths	106	258	392	102	256	257	4	2	135
Person-years	640.2	2,319.3	3,218.6	625.0	2,268.7	1,398.6	15.2	50.6	1,820.0
HR (95% CI)	Ref.	0.66 (0.53-0.83)	0.73 (0.59-0.90)	Ref.	0.68 (0.54-0.86)	1.11 (0.88-1.40)	Ref.	0.15 (0.03-0.83)	0.29 (0.11-0.78)
aHR (95% CI)	Ref.	0.62 (0.49-0.78)	0.77 (0.61-0.97)	Ref.	0.63 (0.50-0.80)	0.79 (0.62-1.00)	Ref.	0.18 (0.03-0.99)	0.26 (0.09-0.72)

Behavior changes (Pre-Post)	Subset of the participants who had screened both before and after the diagnosis (n=5,929)
Behavior changes (Pre-Post)	Persistent poor (poor-poor)	Worsen (moderate-poor, good-poor, good-moderate)	Enhanced (poor-moderate, poor-good, moderate-good)	Persistent moderate/good (moderate-moderate, good-good)
Be a healthy weight
No.	951	597	1,840	2,541
No. of deaths	40	26	126	155
Person-years	1,109.5	697.0	2,208.8	3,039.3
HR (95% CI)	Ref.	1.03 (0.63-1.69)	1.57 (1.10-2.23)	1.40 (0.99-1.98)
aHR (95% CI)	Ref.	0.91 (0.55-1.49)	1.15 (0.80-1.67)	1.14 (0.80-1.63)
Be physically active
No.	1,388	1,054	1,665	1,822
No. of deaths	123	82	90	52
Person-years	1,657.7	1,283.2	1,949.4	2,164.3
HR (95% CI)	Ref.	0.86 (0.65-1.13)	0.63 (0.48-0.82)	0.33 (0.24-0.45)
aHR (95% CI)	Ref.	0.94 (0.71-1.25)	0.73 (0.55-0.96)	0.46 (0.33-0.64)
Limit alcohol consumption
No.	416	484	1,791	3,238
No. of deaths	23	10	108	206
Person-years	482.1	543.7	2,192.3	3,836.5
HR (95% CI)	Ref.	0.39 (0.19-0.82)	1.01 (0.65-1.59)	1.12 (0.73-1.72)
aHR (95% CI)	Ref.	0.35 (0.16-0.73)	0.91 (0.58-1.44)	1.03 (0.66-1.61)
Quit smoking^b)
No.	355	408	1,607	3,559
No. of deaths	35	30	97	185
Person-years	437.7	476.0	1,918.3	4,222.6
HR (95% CI)	Ref.	0.80 (0.49-1.31)	0.64 (0.43-0.94)	0.56 (0.39-0.80)
aHR (95% CI)	Ref.	0.61 (0.37-1.01)	0.56 (0.38-0.83)	0.60 (0.41-0.88)

Table 1. Baseline characteristics of gastric cancer patients at diagnosis from Cancer Public Library Database (2012-2019)

Values are presented as number (%) or mean±SD. AJCC-7, American Joint Committee on Cancer, 7th edition; n, the number of corresponding cases.

Metropolitan cities include Busan, Daegu, Incheon, Gwangju, Daejeon, Ulsan, Sejong in Korea. Other regions include Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju in Korea,

Defined as those who were measured as having ≥ 140 mmHg of systolic blood pressure or ≥ 90 of diastolic blood pressure, or those who self-reported hypertension,

Defined as those who were measured as having ≥ 126 of fasting blood glucose, or those who self-reported the history of diabetes.

Table 2. Cox proportional hazard models demonstrating association between postdiagnosis healthy behaviors and all-cause mortality, stratified by sexa)

aHR, adjusted hazard ratios; CI, confidence interval; HR, hazard ratio; Ref, reference.

The overall model was adjusted for age, sex (men or women), year postdiagnosis, cancer stage (I, II, III, IV or unknown), insurance type (local insured, employee insured, medical-aid beneficiary or missing), income level (decile to tertile; the lowest (tertile 1: 0-3), middle (tertile 2: 4-7), and the highest (tertile 3: 8-10) or missing), and residence (Seoul, metropolitan cities or others). Sex was not adjusted in the sex-stratified models,

Poor for BMI (kg/m²) < 19.0 or BMI (kg/m²) ≥ 23.0 and waist circumference ≥ 90 cm (men) or ≥ 80 cm (women), moderate for BMI (kg/m²) < 19.0 or BMI (kg/m²) ≥ 23.0 and waist circumference < 90 cm (men) or < 80 cm (women), and good for BMI (kg/m²) between 19.0 and 23.0 with waist circumference < 90 cm (men) or < 80 cm (women),

Based on the mean frequencies of moderate to vigorous physical activity per week, poor for 0 day, moderate for 1-4 days, and good for ≥ 5 days,

Based on the total alcohol consumption (drink/day), poor for heavy drinkers (men: ≥ 2, women: ≥ 1), moderate for light drinkers (men: < 2, women: < 1), and good point for non-drinkers,

Based on the smoking status, poor for current smokers, moderate for former smokers, and good for non-smokers.

Table 3. Cox proportional hazard models demonstrating association between behavior changes and all-cause mortalitya)

aHR, adjusted hazard ratios; CI, confidence interval; HR, hazard ratio; Ref, reference.

The model was adjusted for age, sex (men or women), year postdiagnosis, cancer stage (I, II, III, IV or unknown), insurance type (local insured, employee insured, medical-aid beneficiary or missing), income level (decile to tertile; the lowest (tertile 1: 0-3), middle (tertile 2: 4-7), and the highest (tertile 3: 8-10) or missing), and residence (Seoul, metropolitan cities or others),

In the ‘quit smoking’, “Enhanced” only includes poor-moderate, as poor-moderate and moderate-good are logically impossible.