Accessibility settings

This paper is in the following e-collection/theme issue:

Prevention and Health Promotion (2335) Longitudinal and Cohort Studies in Public Health (965) Nutrition, Physical Activity, Healthy Lifestyle for Cancer Patients and Survivors (259) Cardiovascular Disease Prevention (283)

Published on in Vol 12 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/95237, first published .
Elderly woman experiencing chest pain, a potential heart attack symptom in seniors.

Healthy Lifestyle Score and the Risk of Cardiovascular Disease, Cancer, and Mortality in an Asian Population: Prospective Cohort Study

Healthy Lifestyle Score and the Risk of Cardiovascular Disease, Cancer, and Mortality in an Asian Population: Prospective Cohort Study

Authors of this article:

Su Hyun Park1 Author Orcid Image ;   Peh Joo Ho1 Author Orcid Image ;   Xuan Liang1 Author Orcid Image ;   Rob van Dam2 Author Orcid Image ;   Mary F-F Chong1 Author Orcid Image ;   Charlie Guan Yi Lim1 ;   Xueling Sim1 Author Orcid Image ;   Airu Chia1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Saw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive 2, #11-01, Singapore, Singapore, Singapore

    2George Washington University, Washington D.C., DC, United States

    Corresponding Author:

    Su Hyun Park, PhD, MPH


    Background: Healthy lifestyle behaviors are key modifiable factors for the prevention of chronic diseases and premature mortality. Composite lifestyle scores provide a practical framework for capturing the combined impact of multiple health behaviors, but prospective evidence from multiethnic Asian populations remains limited.

    Objective: This study examined the associations between a composite healthy lifestyle score and the risk of cardiovascular diseases, cancer, and all-cause mortality in a multiethnic Asian population.

    Methods: We analyzed prospective data from 9920 participants of the Singapore Multi-Ethnic Cohort who were free of major chronic diseases at baseline. A healthy lifestyle score was constructed from 5 components: BMI, smoking, physical activity, diet quality, and screen time. Scores ranged from 0 to 10, with higher scores reflecting healthier lifestyles. An expanded score, including mental well-being, was examined in a subset. Hazard ratios (HRs) and 95% CIs were estimated using the Cox proportional hazards model for acute myocardial infarction (AMI), stroke, cancer, and all-cause mortality, adjusting for sociodemographic factors, energy intake, family history of cancer or heart disease, and menopause status.

    Results: During a median follow-up of 14 years, 457 AMI cases, 322 stroke cases, 707 cancer incident cases, and 631 deaths were identified. More favorable lifestyle scores were associated with lower risk of cardiovascular outcomes and mortality, consistent with a dose-response relationship. Compared to participants scoring 8 to 10, those with the lowest score (0-3) had more than 2 times the risk of AMI (HR 2.13, 95% CI 1.47-3.07), stroke (HR 3.21, 95% CI 2.12-4.85), and all-cause mortality (HR 2.01, 95% CI 1.46-2.78). In contrast, no significant associations were observed for cancer incidence. Incorporating mental well-being into the lifestyle score yielded similar results.

    Conclusions: Adherence to a healthy lifestyle was associated with a lower risk of AMI, stroke, and mortality in a multiethnic Asian cohort, highlighting the importance of integrated lifestyle approaches for chronic disease prevention.

    JMIR Public Health Surveill 2026;12:e95237

    doi:10.2196/95237

    Keywords

    healthy lifestyle score; mortality; acute myocardial infarction; AMI; stroke; cardiovascular disease; cancer 


    Noncommunicable diseases remain the leading cause of death worldwide, accounting for over 70% of mortality, with cardiovascular diseases (CVDs) causing most premature deaths, followed by cancers [1]. Key modifiable lifestyle factors such as smoking, physical inactivity, and unhealthy diets, along with metabolic risks such as overweight and obesity, which largely stem from these behaviors, are major contributors to the development of noncommunicable diseases [1,2].

    Composite lifestyle scores that integrate multiple lifestyle factors into a single measure can provide a combined impact and holistic assessment of overall lifestyle [3]. Compared to individual risk factors, these composite scores show stronger associations with major chronic disease outcomes and all-cause mortality. Meta-analyses have reported that individuals with the healthiest composite lifestyle scores had a 55% lower risk of all-cause mortality than those with the worst lifestyle scores [4], while the risk reduction was 33% for physical activity alone [5] and 23% for diet alone [6]. This greater risk reduction likely reflects synergistic or multiplicative interactions among lifestyle behaviors, which are often not captured when individual factors are examined separately [7,8].

    Despite robust evidence from Western populations [8,9], studies in Asian populations are comparatively fewer and largely based on East Asian cohorts in China, Japan, or South Korea [10-12]. This limits the generalizability of existing evidence to more diverse or multiethnic Asian contexts such as Singapore given differences in lifestyle behaviors across ethnic groups [13]. Prior cohort studies have rarely incorporated screen-based behavior or related health indicators such as mental well-being despite their relevance to broader lifestyle patterns. Excessive screen time has been associated with greater sedentary behavior, sleep disruption, and poorer mental well-being [14,15], which may collectively increase the risk of major cardiovascular outcomes and mortality [16-21]. A recent analysis of 13,282 adults aged 65 years and older in the Chinese Longitudinal Healthy Longevity Survey found that better subjective well-being (eg, less anxiety and greater happiness) was independently associated with a 14% lower risk of all-cause mortality and a 30% lower risk when combined with healthy lifestyle behaviors. However, the combined effects of lifestyle factors with mental well-being and screen time on major health outcomes have not been well examined in multiethnic Asian populations.

    This study examined the association between a combined lifestyle score incorporating smoking, diet, physical activity, BMI, and screen time and major health outcomes, including all-cause mortality, stroke, acute myocardial infarction (AMI), and cancer, in a multiethnic population. Within individuals with available mental well-being data, the association between major health outcomes and the expanded lifestyle score including mental well-being was studied. In addition, we examined which individual lifestyle component contributed the most to the observed associations, thus providing insights into key targets for prevention and intervention.


    Study Population

    We used data from the Multi-Ethnic Cohort Phase 1 (MEC1), a population-based study of adult residents aged 21 years and older in Singapore that includes 3 major ethnic groups (Chinese, Indian, and Malay). MEC1 baseline data (T1) were collected between 2004 and 2010. Individuals with a history of cancer, CVD, stroke, renal failure, or serious mental illness at recruitment were excluded from cohort enrollment. The MEC1 baseline dataset was formed by combining 2 existing population-based studies conducted between 2004 and 2007: the Singapore Prospective Study Program and the Singapore Cardiovascular Cohort Study, with additional recruitment of participants from 2007 to 2010 (Singapore Consortium of Cohort Studies–Multi-Ethnic Cohort [SCCS-MEC]) through household visits and community outreach to achieve oversampling of Malay and Indian individuals in Singapore. All participants provided written informed consent, including consent for linkage with national disease registries. Detailed information on the cohort has been published previously [22].

    Of the initial 12,988 participants, 12,384 (95.4%) remained after excluding those not from the 3 major ethnic groups (n=33), with missing interview data (n=42), or with preexisting CVD (n=328) or cancer (n=201). After further exclusions of those with missing lifestyle data (n=2376) or covariate data (n=19) and implausible energy intake (≤500 or ≥6000 kcal per day; n=69), 9920 participants were included in the final analysis (Multimedia Appendix 1). The median follow-up duration ranged from 14.1 years for cancer to 14.3 years for all-cause mortality, with 14.2 years for both AMI and stroke. Mental well-being data were available for 4791 participants from a subset of participants (Singapore Prospective Study Program and Singapore Cardiovascular Cohort Study) who completed the 36-item Short Form Health Survey version 2 (SF-36v2) emotional well-being items at baseline; therefore, analyses incorporating mental well-being were restricted to this subset. In the subset with mental well-being data, the median follow-up ranged from 16.1 years (AMI, stroke, and cancer) to 16.2 years (all-cause mortality).

    Construction of a Healthy Lifestyle Score

    Six lifestyle factors were assessed at baseline: BMI, cigarette smoking, physical activity, diet quality, and screen time, with mental well-being available in a subset of the participants. BMI was calculated from measured height and weight, while the other factors were self-reported via an interviewer-administered questionnaire [22]. Each factor was categorized into 3 levels and assigned 0 to 2 points, with higher scores representing healthier behaviors. Three levels were chosen to balance the need to capture meaningful variation with the need to maintain sufficient sample size within each category.

    BMI was classified using established Asian cutoffs, defined as less than 23.0 kg/m2 (2 points), 23.0 to 24.9 kg/m2 (1 point), and 25.0 kg/m2 or higher (0 points) [23,24]. Smoking status was categorized as “never” (2 points), “former” (1 point), or “current” (0 points). Leisure-time physical activity was assessed using a physical activity questionnaire [25] and expressed in metabolic equivalent of task (MET) hours per week. Participants were categorized into 7.5 or more MET hours per week (2 points), 0 to less than 7.5 MET hours per week (1 point), and none (0 points), consistent with the World Health Organization recommendation of 150 minutes or more of moderate-intensity physical activity per week [26]. Dietary intake was assessed using the validated semiquantitative food frequency questionnaire (FFQ) [27], and diet quality was evaluated using the Alternative Healthy Eating Index (AHEI) 2010 [19], with higher scores indicating better diet quality. Participants were categorized into tertiles: highest (2 points), intermediate (1 point), and lowest (0 points). Screen time was assessed through the amount of time spent watching television or playing computer or handheld video games daily. Participants were categorized into less than 2 hours per day (2 points), 2 to 4 hours per day (1 point), and more than 4 hours per day (0 points). In a subset of the population, mental well-being was assessed using the SF-36v2 emotional well-being subscale derived from 5 items assessing nervousness, low mood (being downhearted or depressed), calmness, and happiness over the previous 4 weeks, with higher scores indicating better mental well-being [28]. Scores were transformed into a norm-based T-score (mean 50, SD 10) following the SF-36v2 scoring manual [28]. Participants were categorized into tertiles: highest (2 points), intermediate (1 point), and lowest (0 points). As mental well-being data were available only for a subset of participants (n=4791), the main analyses were conducted using a 5-factor score in the entire cohort, while mental well-being was incorporated in additional analyses in the smaller subset.

    The primary lifestyle score was calculated by summing all 5 factors, with possible scores ranging from 0 to 10. Participants were further grouped into 4 categories: score of 0 to 3 (least favorable lifestyle profile), score of 4 or 5, score of 6 or 7, and score of 8 to 10 (most favorable lifestyle profile; Table 1). In the subset with mental well-being data, an extended 6-factor lifestyle score was calculated by additionally including mental well-being, contributing scores from 0 to 2 to the previous 5-factor lifestyle score. This extended 6-factor score had a wider possible range from 0 to 12 and a different observed distribution. Participants were grouped separately into 4 ordered categories: scores of 1 to 4, 5 and 6, 7 and 8, and 9 to 12.

    Table 1. Definition of the healthy lifestyle score.
    Lifestyle factor and pointsDescription
    BMI
    0Obese (≥25.0 kg/m2)
    1Overweight (23.0‐24.9 kg/m2)
    2Normal (<23.0 kg/m2)
    Cigarette smoking
    0Current smoker
    1Former smoker
    2Never smoker
    Physical activity
    0None (0 METa h per wk)
    1Low (>0 to <7.5 MET h per wk)
    2Adequate (≥7.5 MET h per wk)
    Diet quality
    0Lowest tertile (poorest diet quality)
    1Middle tertile
    2Highest tertile
    Screen time
    0High (>4 h per d)
    1Moderate (2-4 h per d)
    2Low (<2 h per d)
    Mental well-being
    0Lowest tertile (poorest mental well-being)
    1Middle tertile
    2Highest tertile

    aMET: metabolic equivalent of task.

    Outcome Ascertainment

    Incident cases of AMI, stroke, all-site cancer, and deaths were ascertained through linkage with national disease registries maintained by the National Registry of Diseases Office, including the Singapore Myocardial Infarction Registry, Singapore Stroke Registry, Singapore Cancer Registry, and Singapore Death Registry.

    Follow-up time was calculated from the date of baseline interview to the date of first occurrence of the outcome of interest, death, or end of follow-up (December 31, 2021), whichever came first.

    Covariates

    Covariates included age, sex, ethnicity, educational level, income, total energy intake, family history of cancer or heart disease, and menopausal status in women. Age was categorized as 35 years or younger, 36 to 45 years, 46 to 55 years, and 56 years or older. Sex was defined as male or female. Ethnicity was classified as Chinese, Malay, or Indian. Educational attainment was grouped into 4 categories: primary or lower, secondary, postsecondary, and university or higher. Household income was categorized into predefined levels, with a separate category created for participants who refused to answer, did not know, or had missing information.

    Total energy intake (kcal per day) was derived from the FFQ administered at baseline and modeled as a continuous variable. Family history of cancer or heart disease was coded as “yes” or “no” based on self-report. Menopausal status was classified as premenopausal or postmenopausal among women, and male participants were coded as a separate category to retain all participants in the model.

    Ethical Considerations

    Ethics approval was obtained from the Institutional Review Board of National University of Singapore (NUS-IRB-2022-27). All participants provided written informed consent, including consent for linkage with national disease registries. Research data used for this secondary analysis were deidentified and key coded, with access restricted to authorized members of the research team.

    Statistical Analysis

    Descriptive statistics were presented according to the categories of the lifestyle score. For continuous variables, P values were estimated by modeling the median value of each lifestyle score category as a continuous variable in linear regression. For categorical variables, P values were calculated using the Cochran-Armitage trend test. To assess potential selection bias related to missing lifestyle data, baseline characteristics were compared between participants included in the final analytic sample and those excluded because of missing lifestyle data.

    Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% CIs for associations between the lifestyle score and each outcome, including incident AMI, incident stroke, incident cancer, and all-cause mortality. The proportionality assumption was assessed using Schoenfeld residuals; no violations were detected. Tests for linear trend were performed by modeling lifestyle score categories as a continuous variable in the regression model. All models were adjusted for age, sex, ethnicity, educational level, income, total energy intake, family history of cancer or heart disease, and menopausal status.

    Additional analyses were conducted in the subset of participants with available mental well-being data by incorporating mental well-being as a sixth component of the lifestyle score. To facilitate comparison between the primary and extended lifestyle scores, both scores were also modeled as continuous variables, with HRs estimated per 1-point increase. Ethnicity-stratified analyses were performed, and multiplicative interaction terms between the lifestyle score and ethnicity were included in the Cox models to assess potential effect modification by ethnicity. To assess whether the associations were driven by any single component of the lifestyle score, leave-one-out analyses were performed by recalculating the lifestyle score after omitting one component at a time.


    Differences between included participants and those excluded because of missing lifestyle data were observed for age, educational level, household income, total energy intake, and family history of cancer or heart disease, whereas sex, ethnicity, and menopausal status were similar between groups (Multimedia Appendix 2).

    Table 2 shows the baseline characteristics of the participants according to the healthy lifestyle score. Of the 9920 participants, 4254 (42.9%) were male, and most (n=4480, 45.2%) were Chinese, followed by Indian (n=2790, 28.1%) and Malay (n=2650, 26.7%). Most participants (2923/9920, 29.5%) were aged 46 to 55 years. Participants with the least favorable lifestyle profile (score of 0‐3) were younger, more often male, and of non-Chinese ethnicity and had lower income and educational attainment (P<.01 in all cases). Compared with participants with the least favorable lifestyle profile, those in the highest category (score of 8‐10) had lower BMI (median 21.9, IQR 20.1-23.7 vs 27.4, IQR 25.3-30.3 kg/m2); comprised fewer smokers (18/2605, 0.7% vs 479/645, 74.3%); engaged in higher levels of leisure-time physical activity (median 13.6, IQR 8.0-24.5 vs 0.0, IQR 0.0-0.0 MET hours per week); and reported better diet quality (median AHEI score 55.8, IQR 50.5-60.2 vs 41.4, IQR 36.3-45.8), shorter screen time (median 0.0, IQR 0.5-1.5 vs 1.0, IQR 0.0-2.0 hours per day), and better mental well-being (mean T-score 50.9, SD 9.3 vs 48.9, SD 11.2; P<.001 in all cases). In contrast, total energy intake, menopausal status, and family history of cancer or heart disease did not differ significantly across categories.

    Table 2. Baseline characteristics of study participants according to healthy lifestyle score (N=9920).a
    TotalHealthy lifestyle scoreP value
    0-3 (n=645)4-5 (n=2657)6-7 (n=4013)8-10 (n=2605)
    Age (y), n (%).01
     ≤352358 (23.8)208 (32.2)615 (23.1)925 (23.1)610 (23.4)
     36-452606 (26.3)173 (26.8)687 (25.9)1043 (26)703 (27) 
     46-552923 (29.5)163 (25.3)848 (31.9)1168 (29.1)744 (28.6) 
     562033 (20.5)101 (15.7)507 (19.1)877 (21.9)548 (21) 
    Sex, n (%)<.001
     Male4254 (42.9)437 (67.8)1227 (46.2)1582 (39.4)1008 (38.7)
     Female5666 (57.1)208 (32.2)1430 (53.8)2431 (60.6)1597 (61.3) 
    Ethnicity, n (%)<.001
     Chinese4480 (45.2)202 (31.3)910 (34.2)1833 (45.7)1535 (58.9)
     Malay2650 (26.7)253 (39.2)950 (35.8)1027 (25.6)420 (16.1) 
     Indian2790 (28.1)190 (29.5)797 (30)1153 (28.7)650 (25) 
    Household monthly income (SGD; SGD 1=US $0.78 as of October 18, 2026), n (%)<.001
     <20002309 (23.3)247 (38.3)815 (30.7)882 (22)365 (14)
     2000-39992579 (26)189 (29.3)729 (27.4)1073 (26.7)588 (22.6) 
     4000-59991624 (16.4)68 (10.5)385 (14.5)654 (16.3)517 (19.8) 
     60001384 (14)43 (6.7)266 (10)554 (13.8)521 (20) 
     Refused to answer or did not know2024 (20.4)98 (15.2)462 (17.4)850 (21.2)614 (23.6) 
    Educational level, n (%)<.001
     Primary or lower2753 (27.8)246 (38.1)979 (36.8)1086 (27.1)442 (17)
     Secondary3602 (36.3)243 (37.7)997 (37.5)1484 (37)878 (33.7) 
     Postsecondary2356 (23.8)139 (21.6)528 (19.9)961 (23.9)728 (27.9) 
     University or higher1209 (12.2)17 (2.6)153 (5.8)482 (12)557 (21.4) 
    Total energy intake (kcal per d), median (IQR)2111.8 (1651.7-2671.6)2185.7 (1689.3-2829.0)2094.2 (1618.1-2635.1)5 (1625.7-2673.9)2149.3 (1725.6-2670.5)<.001
    Family history of cancer or heart disease, n (%).16
     No7196 (72.5)464 (71.9)1903 (71.6)2918 (72.7)1911 (73.4)
     Yes2724 (27.5)181 (28.1)754 (28.4)1095 (27.3)694 (26.6) 
    Menopause statusb, n/N (%).10
     Premenopausal3632/5666 (64.1)143/208 (68.8)895/1430 (62.6)1533/2431 (63.1)1061/1597 (66.4)
     Postmenopausal2034/5666 (35.9)65/208 (31.3)535/1430 (37.4)898/2431 (36.9)536/1597 (33.6) 
    BMI (kg/m2), median (IQR)24.3 (21.5-27.7)27.4 (25.3-30.3)26.6 (24.1-29.6)24.1 (21.4-27.2)21.9 (20.1-23.7)<.001
    Smoking status, n (%)<.001
     Never7657 (77.2)108 (16.7)1682 (63.3)3369 (84)2498 (95.9)
     Former695 (7)58 (9)263 (9.9)285 (7.1)89 (3.4)
     Current1568 (15.8)479 (74.3)712 (26.8)359 (8.9)18 (0.7) 
    Screen time (h per d), median (IQR)0.7 (0.0-2.0)1.0 (0.0-3.0)1.0 (0.0-2.0)0.8 (0.0-2.0)0.0 (0.5-1.5)<.001
    Physical activity (METc h per wk), median (IQR)4.7 (0.0-15.0)0.0 (0.0-0.0)0.0 (0.0-5.3)4.5 (0.0-14.2)13.6 (8.0-24.5)<.001
    Diet quality (AHEId score), median (IQR)50.1 (43.6-56.6)41.4 (36.3-45.8)45.4 (39.9-51.4)50.6 (45.1-56.5)55.8 (50.5-60.2)<.001
    Mental well-being (n=4791), mean (SD)50.3 (10.0)48.9 (11.2)49.4 (10.8)50.5 (9.8)50.9 (9.3)<.001

    aP values were estimated by modeling the median value of each lifestyle score category as a continuous variable in linear regression for continuous outcomes and by using the Cochran-Armitage test for categorical outcomes.

    bOnly in women.

    cMET: metabolic equivalent of task.

    dAHEI: Alternative Healthy Eating Index.

    The associations between the healthy lifestyle score and risk of health outcomes are shown in Table 3. Over a median follow-up of 14 years, 457 AMI cases, 322 stroke cases, 707 cancer cases, and 631 all-cause deaths were documented. In the adjusted model, participants with the least favorable lifestyle profile (score of 0‐3) had higher risks of AMI (HR 2.13, 95% CI 1.47‐3.07), stroke (HR 3.21, 95% CI 2.12‐4.85), and all-cause mortality (HR 2.01, 95% CI 1.46‐2.78) than those in the most favorable category (score of 8-10). Significant inverse dose-response trends were observed for these outcomes (P<.01 in all cases). No significant association was observed for cancer (HR 1.28, 95% CI 0.91‐1.79). Kaplan-Meier curves showed graded separation for mortality, AMI, and stroke, whereas cancer curves largely overlapped (Multimedia Appendix 3).

    Table 3. Hazard ratios (HRs) for the associations between healthy lifestyle score and the risk of all-cause mortality, stroke, acute myocardial infarction (AMI), and cancer adjusting for age, sex, ethnicity, income, educational level, total energy intake, family history of cancer or heart disease, and menopause status.
    Lifestyle scoresAll-cause death (n=631; median follow-up 14.3 y)AMI (n=457; median follow-up 14.2 y)Stroke (n=322; median follow-up 14.2 y)Cancer (n=707; median follow-up 14.1 y)
    Cases, nHR (95% CI)aCases, nHR (95% CI)aCases, nHR (95% CI)aCases, nHR (95% CI)b
    0-3642.01 (1.46-2.78)542.13 (1.47-3.07)453.21 (2.12-4.85)461.28 (0.91-1.79)
    4-52051.64 (1.28-2.09)1561.71 (1.28-2.28)971.54 (1.09-2.18)1811.09 (0.87-1.35)
    6-72571.44 (1.14-1.81)1721.38 (1.05-1.81)1251.36 (0.99-1.88)3031.14 (0.95-1.38)
    8-101051.00751.00551.001771.00

    aP<.001.

    bP=.24.

    In analyses incorporating the mental health component into the lifestyle score, results were consistent. Participants with lower healthy lifestyle scores remained at higher risk of all-cause mortality and AMI, with similar estimates for these outcomes, while the association with stroke was slightly attenuated (HR 2.14, 95% CI 1.32‐3.46). No clear dose-response association was observed for overall cancer incidence (Multimedia Appendix 4).

    When modeled as continuous variables, the primary lifestyle score and the score including mental well-being showed similar inverse associations with all-cause mortality, AMI, and stroke. The primary score was not associated with overall cancer incidence, whereas the score including mental well-being showed a modest inverse association with cancer (Multimedia Appendix 5).

    Ethnicity-stratified analyses were conducted to assess potential effect modification by ethnicity. No significant interactions were observed for all-cause mortality, AMI, or stroke (P=.29, .41, and .43, respectively), whereas an interaction was observed for overall cancer incidence (P=.02). However, stratified analyses by ethnicity did not show significant dose-response trends within any ethnic group (Multimedia Appendix 6).

    In leave-one-out analyses (Figure 1), exclusion of smoking attenuated the association with all-cause mortality (HR 1.46, 95% CI 0.85‐2.48), indicating that smoking was a major contributor to this association. For AMI, effect estimates remained largely significant across all exclusions, suggesting that the association was not driven by any single lifestyle component. For stroke, exclusion of smoking (HR 1.38, 95% CI 0.71‐2.67) or screen time (HR 1.57, 95% CI 0.89‐2.75) resulted in attenuation, indicating that these factors may play an important role in the observed associations. For cancer, the lifestyle score excluding physical activity was associated with risk (HR 1.56, 95% CI 1.02‐2.38), while exclusion of diet quality (AHEI score) showed a borderline significant association (HR 1.47, 95% CI 1.00‐2.15).

    ‎
    Figure 1. Leave-one-out analyses of the healthy lifestyle score in relation to major health outcomes. The healthy lifestyle score ranged from 0 to 10 in the full model and from 0 to 8 when an individual component was excluded. Higher scores indicate healthier lifestyle profiles. The points represent hazard ratios, and the error bars indicate 95% confidence intervals. AHEI: Alternative Healthy Eating Index; AMI: acute myocardial infarction; PA: physical activity.

    Principal Findings

    In this multiethnic Asian cohort with over 14 years of follow-up, adherence to a healthy lifestyle as measured using a composite score was associated with substantially lower risks of all-cause mortality, AMI, and stroke. Participants with the least favorable lifestyle profiles had twice the risk of AMI and all-cause mortality and over 3 times the risk of stroke compared with those with the most favorable profiles. Results were consistent when mental well-being was incorporated in the lifestyle score. No significant associations were observed for overall cancer incidence. These findings highlight the importance of healthy lifestyle behaviors for cardiovascular health and longevity in Asian populations.

    Comparison With Previous Studies

    Our findings are consistent with those of previous cohort studies reporting that composite healthy lifestyle scores are inversely associated with mortality and CVD outcomes in both Western and Asian populations [29]. Large cohorts from Europe and the United States have demonstrated that combinations of nonsmoking, healthy body weight, regular physical activity, light-to-moderate alcohol consumption, and high-quality diet are associated with reductions in all-cause and cardiovascular mortality [30,31]. Comparable associations have been documented in East Asian cohorts, including China, Taiwan, South Korea, and Japan, where higher lifestyle scores incorporating BMI, smoking, physical activity, diet, and alcohol consumption were observed [32-35]. In our study, participants with the least favorable lifestyle profiles had approximately 2.0 times the risk of all-cause mortality, 2.1 times the risk of AMI, and 3.2 times the risk of stroke, broadly consistent with meta-analyses of prospective cohort studies reporting 2.2 times higher all-cause mortality and 2.2 to 3.4 times higher risk of various cardiovascular outcomes [4]. Our study extends this literature to a multiethnic Southeast Asian population and incorporates screen-based sedentary behavior, assessed as television viewing and computer or handheld video game use, into the composite lifestyle score.

    Interpretation and Implications

    A notable contribution of this study is the integration of screen time into the composite lifestyle score. Sedentary behavior, including prolonged screen time, has been increasingly recognized as an independent risk factor for cardiometabolic diseases. Epidemiological evidence across multiple cohorts has shown that increased leisure screen time is associated with greater cardiometabolic risk in both adolescents and adults, as well as with elevated risks of coronary artery disease and mortality [21,36,37]. Consistent with these findings, our leave-one-out analysis showed that removing screen time from the score substantially attenuated the association with stroke, suggesting that screen time may be particularly relevant for cerebrovascular risk. Potential mechanisms include impaired circulation, central adiposity, dysregulated glucose metabolism, elevated blood pressure, and inflammation, all of which are closely linked to ischemic stroke [38-41].

    Although there is accumulating evidence suggesting that psychological distress and low mental well-being are associated with higher risks of mortality and CVDs [42-44], mental well-being has rarely been incorporated into lifestyle indexes. While mental well-being is not a lifestyle behavior per se, it is largely shaped by them, and including them in the combined lifestyle score allows for a broader assessment of health. In our study, mental well-being was assessed using the SF-36v2 mental health subscale, which has been used to predict mortality and CVD outcomes in prior prospective cohorts [45]. In the subgroup with available data, adding mental well-being to the score did not substantially change the overall patterns of association. The estimates remained similar for all-cause mortality and slightly attenuated for AMI and stroke but were statistically significant despite the smaller sample size. These findings suggest that mental well-being represents a complementary domain of lifestyle assessment, capturing psychosocial aspects of health that may support overall disease prevention and well-being.

    The composite lifestyle score was designed to capture the cumulative influence of multiple lifestyle-related risk factors that commonly co-occur in real-world settings as compared to considering each lifestyle component independently. Smoking, poor diet quality, lower physical activity, and sedentary behaviors may cluster within broader unhealthy lifestyle patterns, and the score provides a practical summary measure of this overall lifestyle risk. In sensitivity analyses excluding smoking, the associations with all-cause mortality and stroke were attenuated and no longer statistically significant, suggesting that smoking was a particularly influential component for these outcomes. This pattern aligns with extensive evidence showing that smoking promotes endothelial dysfunction, systemic inflammation, oxidative stress, and thrombotic pathways that accelerate atherosclerosis and increase risks of ischemic stroke and premature death [46-49]. These findings highlight the importance of smoking prevention and cessation for reducing the risks of premature mortality and stroke.

    There was no significant evidence of interaction by ethnicity for the associations with all-cause mortality, AMI, and stroke. Although an interaction was observed for overall cancer incidence, the ethnicity-stratified analyses did not show clear dose-response trends. This finding should be interpreted cautiously. The null association between the lifestyle score and overall cancer incidence should also be interpreted with caution. Cancer encompasses multiple diseases with distinct etiologies and typically long latency periods, and aggregating all cancer types into a single outcome may obscure lifestyle-related associations that differ by cancer site. Furthermore, the relatively young baseline age of our cohort and comparatively short follow-up for cancer outcomes resulted in a limited number of cancer events during follow-up, reducing the power to detect associations.

    Limitations

    This study has several limitations. First, the lifestyle score was constructed as an unweighted index summing all lifestyle components equally and assuming each factor had equal importance. Previous studies have examined both weighted and unweighted indexes, showing that both effectively predict CVD risk [50]. The unweighted index is outcome agnostic, which is more practical and can be applied to a broader range of health outcomes. Second, lifestyle behaviors were primarily self-reported, which may introduce recall error or reporting bias. However, such errors are likely nondifferential as outcomes were assessed prospectively. The physical activity questionnaire and FFQ have been validated in independent samples of the local population with similar characteristics [22,27,51]. In addition, differences in several baseline characteristics between included participants and those excluded because of missing lifestyle data may have introduced selection bias. Third, lifestyle behaviors and covariates were measured at baseline only, and changes in these behaviors during follow-up may have introduced nondifferential misclassification that biased the association toward the null. Fourth, alcohol consumption was not included as a separate lifestyle component as it was already incorporated into the AHEI 2010 score to avoid double counting. Fifth, although we adjusted for multiple confounders, our findings could still be influenced by residual confounding and unmeasured factors. While the median follow-up of approximately 14 years allowed for assessment of long-term outcomes, longer follow-up may be needed to further clarify associations, particularly for cancer outcomes with longer latency. Sixth, cancer was analyzed as a single combined end point. Although overall cancer incidence provides a broad assessment of cancer burden alongside other major chronic disease outcomes, cancer comprises a heterogeneous group of diseases with distinct etiologies, and combining all incident cancers may obscure site-specific associations with lifestyle factors. Therefore, findings for overall cancer incidence should be interpreted cautiously. Finally, as this study was conducted in a multiethnic Asian cohort, the findings may not be generalizable to other populations with different sociodemographic or cultural contexts.

    Conclusions

    Our findings highlight the importance of combined lifestyle behaviors, including screen-based sedentary behavior, for cardiovascular and overall health. Incorporating mental well-being yielded similar results, suggesting that a broader conceptualization of lifestyle may be relevant for chronic disease prevention. These results highlight opportunities for comprehensive lifestyle interventions and public health strategies to reduce CVD and premature mortality in Asian populations.

    Acknowledgments

    The authors thank all participants, the study team, and the investigators for their research contributions. The Multi-Ethnic Cohort Phase 1 study is supported by individual research and clinical scientist award schemes from the National Medical Research Council and the Biomedical Research Council of Singapore and infrastructure funding from the Singapore Ministry of Health (Population Health Metrics and Analytics), National University of Singapore, and the National University Health System of Singapore. The authors thank the National Registry of Diseases Office for providing the record linkage data.

    Generative AI Use

    The authors attest that no generative AI or AI-assisted tools were used in developing and writing this manuscript.

    Funding

    This study was funded by the Saw Swee Hock School of Public Health ground-up research seed fund 2021 to support academic or operational insights influencing national policies or programs in the areas of population or public health in Singapore (grant principal investigator: AC).

    Data Availability

    The datasets generated or analyzed during this study are available from the Singapore Population Health Studies [52] on reasonable request and with appropriate institutional approvals.

    Authors' Contributions

    Conceptualization: SHP, AC

    Funding acquisition: AC

    Investigation: SHP

    Methodology: SHP, AC, PJH

    Resources: CGYL, XS

    Software: SHP

    Supervision: RvD, MFFC, XS

    Visualization: PJH

    Writing—original draft: SHP, XL, AC

    Writing—review and editing: SHP, AC, PJH, RvD, MFFC, CGYL, XS

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Study flowchart.

    DOCX File, 17 KB

    Multimedia Appendix 2

    Baseline characteristics of participants included in the final analytic sample and those excluded because of missing lifestyle data.

    DOCX File, 17 KB

    Multimedia Appendix 3

    Kaplan-Meier curves for all-cause death, acute myocardial infarction, stroke, and cancer incidence.

    DOCX File, 83 KB

    Multimedia Appendix 4

    Associations between healthy lifestyle score (including mental well-being) and all-cause mortality, stroke, acute myocardial infarction, and cancer (n=4791).

    DOCX File, 16 KB

    Multimedia Appendix 5

    Associations between both the primary lifestyle score and the lifestyle score including mental well-being and all-cause mortality, acute myocardial infarction, stroke, and overall cancer incidence.

    DOCX File, 16 KB

    Multimedia Appendix 6

    Associations between the healthy lifestyle score and all-cause mortality, acute myocardial infarction, stroke, and cancer stratified by ethnicity.

    DOCX File, 19 KB

    Checklist 1

    STROBE checklist.

    DOCX File, 33 KB
    1. Noncommunicable diseases. World Health Organization. Sep 25, 2025. URL: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [Accessed 2026-10-19]
    2. GBD 2023 Disease and Injury and Risk Factor Collaborators. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. Oct 18, 2025;406(10513):1873-1922. [CrossRef] [Medline]
    3. Loef M, Walach H. The combined effects of healthy lifestyle behaviors on all cause mortality: a systematic review and meta-analysis. Prev Med. Sep 2012;55(3):163-170. [CrossRef] [Medline]
    4. Zhang YB, Pan XF, Chen J, et al. Combined lifestyle factors, all-cause mortality and cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies. J Epidemiol Community Health. Jan 2021;75(1):92-99. [CrossRef] [Medline]
    5. Nocon M, Hiemann T, Müller-Riemenschneider F, Thalau F, Roll S, Willich SN. Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil. Jun 2008;15(3):239-246. [CrossRef] [Medline]
    6. Onvani S, Haghighatdoost F, Surkan PJ, Larijani B, Azadbakht L. Adherence to the Healthy Eating Index and Alternative Healthy Eating Index dietary patterns and mortality from all causes, cardiovascular disease and cancer: a meta-analysis of observational studies. J Hum Nutr Diet. Apr 2017;30(2):216-226. [CrossRef] [Medline]
    7. Li Y, Fan X, Wei L, Yang K, Jiao M. The impact of high-risk lifestyle factors on all-cause mortality in the US non-communicable disease population. BMC Public Health. Mar 2, 2023;23(1):422. [CrossRef]
    8. Ding J, Fu R, Yuan T, Brenner H, Hoffmeister M. Lifestyle scores and their potential to estimate the risk of multiple non-communicable disease-related endpoints: a systematic review. BMC Public Health. Jan 23, 2025;25(1):293. [CrossRef] [Medline]
    9. Behrens G, Fischer B, Kohler S, Park Y, Hollenbeck AR, Leitzmann MF. Healthy lifestyle behaviors and decreased risk of mortality in a large prospective study of U.S. women and men. Eur J Epidemiol. May 2013;28(5):361-372. [CrossRef] [Medline]
    10. Tamakoshi A, Tamakoshi K, Lin Y, Yagyu K, Kikuchi S, JACC Study Group. Healthy lifestyle and preventable death: findings from the Japan Collaborative Cohort (JACC) Study. Prev Med. May 2009;48(5):486-492. [CrossRef] [Medline]
    11. Zhu N, Yu C, Guo Y, et al. Adherence to a healthy lifestyle and all-cause and cause-specific mortality in Chinese adults: a 10-year prospective study of 0.5 million people. Int J Behav Nutr Phys Act. Nov 4, 2019;16(1):98. [CrossRef] [Medline]
    12. Yun JE, Won S, Kimm H, Jee SH. Effects of a combined lifestyle score on 10-year mortality in Korean men and women: a prospective cohort study. BMC Public Health. Aug 20, 2012;12(1):673. [CrossRef] [Medline]
    13. Seah JY, Sim X, Khoo CM, Tai ES, van Dam RM. Differences in type 2 diabetes risk between East, South, and Southeast Asians living in Singapore: the Multi-Ethnic Cohort. BMJ Open Diabetes Res Care. Jul 2023;11(4):e003385. [CrossRef] [Medline]
    14. Pieh C, Humer E, Hoenigl A, et al. Smartphone screen time reduction improves mental health: a randomized controlled trial. BMC Med. Feb 21, 2025;23(1):107. [CrossRef] [Medline]
    15. Santos RM, Ventura SA, Nogueira YJ, et al. The associations between screen time and mental health in adults: a systematic review. J Technol Behav Sci. Feb 2024;9(4):825-845. [CrossRef]
    16. Zhu C, Lian Z, Sun J, et al. Combined lifestyle habits, subjective well-being, and all-cause mortality among older adults in China. BMC Geriatr. Aug 30, 2025;25(1):675. [CrossRef] [Medline]
    17. Kubzansky LD, Huffman JC, Boehm JK, et al. Positive psychological well-being and cardiovascular disease: JACC Health Promotion Series. J Am Coll Cardiol. Sep 18, 2018;72(12):1382-1396. [CrossRef] [Medline]
    18. Zuccarella-Hackl C, Princip M, Sivakumar S, von Känel R. Positive psychological well-being and cardiovascular health. Front Psychiatry. Oct 25, 2024;15:1443978. [CrossRef] [Medline]
    19. Chiuve SE, Fung TT, Rimm EB, et al. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr. Jun 2012;142(6):1009-1018. [CrossRef] [Medline]
    20. Qiu Z, Jia X, Fu Y, Yang Y. Screen time in the development of cardiovascular diseases: a two-sample Mendelian randomization study. Nutr Metab Cardiovasc Dis. Mar 2024;34(3):706-717. [CrossRef] [Medline]
    21. Stamatakis E, Hamer M, Dunstan DW. Screen-based entertainment time, all-cause mortality, and cardiovascular events: population-based study with ongoing mortality and hospital events follow-up. J Am Coll Cardiol. Jan 18, 2011;57(3):292-299. [CrossRef] [Medline]
    22. Tan KH, Tan LW, Sim X, et al. Cohort profile: the Singapore Multi-Ethnic Cohort (MEC) study. Int J Epidemiol. Jun 1, 2018;47(3):699-699j. [CrossRef] [Medline]
    23. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. Jan 10, 2004;363(9403):157-163. [CrossRef] [Medline]
    24. Pan WH, Yeh WT. How to define obesity? Evidence-based multiple action points for public awareness, screening, and treatment: an extension of Asian-Pacific recommendations. Asia Pac J Clin Nutr. 2008;17(3):370-374. [Medline]
    25. Müller-Riemenschneider F, Hong Y, Tan KH, van Dam RM, Uijtdewilligen L. The association of different types of leisure time physical activities with cardiometabolic outcomes in Singapore-findings from the Multi-Ethnic Cohort study. Int J Environ Res Public Health. Dec 3, 2020;17(23):9030. [CrossRef] [Medline]
    26. Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. Dec 2020;54(24):1451-1462. [CrossRef] [Medline]
    27. Deurenberg-Yap M, Li T, Tan WL, van Staveren WA, Deurenberg P. Validation of a semiquantitative food frequency questionnaire for estimation of intakes of energy, fats and cholesterol among Singaporeans. Asia Pac J Clin Nutr. Dec 2000;9(4):282-288. [CrossRef] [Medline]
    28. Ware JE, Kosinski M, Dewey JE. How to Score Version 2 of the SF-36 Health Survey: Standard & Acute Forms. QualityMetric Incorporated; 2001. URL: https://archive.org/details/howtoscoreversio0000ware/page/n9/mode/2up [Accessed 2026-10-28]
    29. Lv J, Yu C, Guo Y, et al. Adherence to healthy lifestyle and cardiovascular diseases in the Chinese population. J Am Coll Cardiol. Mar 7, 2017;69(9):1116-1125. [CrossRef] [Medline]
    30. van Dam RM, Li T, Spiegelman D, Franco OH, Hu FB. Combined impact of lifestyle factors on mortality: prospective cohort study in US women. BMJ. Sep 16, 2008;337:a1440. [CrossRef] [Medline]
    31. Ford ES, Zhao G, Tsai J, Li C. Low-risk lifestyle behaviors and all-cause mortality: findings from the National Health and Nutrition Examination Survey III Mortality Study. Am J Public Health. Oct 2011;101(10):1922-1929. [CrossRef] [Medline]
    32. Lee DH, Nam JY, Kwon S, et al. Lifestyle risk score and mortality in Korean adults: a population-based cohort study. Sci Rep. Jun 24, 2020;10(1):10260. [CrossRef] [Medline]
    33. Lo WC, Hu TH, Shih CY, Lin HH, Hwang JS. Impact of healthy lifestyle factors on life expectancy and lifetime health care expenditure: nationwide cohort study. JMIR Public Health Surveill. Jul 17, 2024;10:e57045. [CrossRef] [Medline]
    34. Nechuta SJ, Shu XO, Li HL, et al. Combined impact of lifestyle-related factors on total and cause-specific mortality among Chinese women: prospective cohort study. PLoS Med. Sep 14, 2010;7(9):e1000339. [CrossRef] [Medline]
    35. Pan XF, Li Y, Franco OH, Yuan JM, Pan A, Koh WP. Impact of combined lifestyle factors on all-cause and cause-specific mortality and life expectancy in Chinese: the Singapore Chinese Health Study. J Gerontol A Biol Sci Med Sci. Oct 15, 2020;75(11):2193-2199. [CrossRef] [Medline]
    36. Jingjie W, Yang L, Jing Y, Ran L, Yiqing X, Zhou N. Sedentary time and its association with risk of cardiovascular diseases in adults: an updated systematic review and meta-analysis of observational studies. BMC Public Health. Feb 12, 2022;22(1):286. [CrossRef] [Medline]
    37. McDonnell MN, Hillier SL, Judd SE, Yuan Y, Hooker SP, Howard VJ. Association between television viewing time and risk of incident stroke in a general population: results from the REGARDS study. Prev Med. Jun 2016;87:1-5. [CrossRef] [Medline]
    38. Horner D, Jahn M, Bønnelykke K, et al. Screen time is associated with cardiometabolic and cardiovascular disease risk in childhood and adolescence. J Am Heart Assoc. Aug 19, 2025;14(16):e041486. [CrossRef] [Medline]
    39. Pinto AJ, Bergouignan A, Dempsey PC, et al. Physiology of sedentary behavior. Physiol Rev. Oct 1, 2023;103(4):2561-2622. [CrossRef] [Medline]
    40. Zheng C, Zhang X, Sheridan S, et al. Effect of sedentary behavior interventions on vascular function in adults: a systematic review and meta-analysis. Scand J Med Sci Sports. Jul 2021;31(7):1395-1410. [CrossRef] [Medline]
    41. Howard BJ, Balkau B, Thorp AA, et al. Associations of overall sitting time and TV viewing time with fibrinogen and C reactive protein: the AusDiab study. Br J Sports Med. Feb 2015;49(4):255-258. [CrossRef] [Medline]
    42. Zhu C, Lian Z, Huang Y, Zhong Q, Wang J. Association between subjective well-being and all-cause mortality among older adults in China. BMC Psychiatry. Aug 25, 2023;23(1):624. [CrossRef] [Medline]
    43. Alimujiang A, Strecher V, McLean K, Mondul AM, Pearce CL, Mukherjee B. Decomposing the association of psychosocial wellbeing with all-cause mortality: the mediating role of physical health and lifestyle factors. Soc Psychiatry Psychiatr Epidemiol. Jun 2025;60(6):1445-1455. [CrossRef] [Medline]
    44. Machado MO, Veronese N, Sanches M, et al. The association of depression and all-cause and cause-specific mortality: an umbrella review of systematic reviews and meta-analyses. BMC Med. Jul 20, 2018;16(1):112. [CrossRef] [Medline]
    45. Zhang JP, Pozuelo L, Brennan DM, Hoar B, Hoogwerf BJ. Association of SF-36 with coronary artery disease risk factors and mortality: a PreCIS study. Prev Cardiol. 2010;13(3):122-129. [CrossRef] [Medline]
    46. Münzel T, Hahad O, Kuntic M, Keaney JF, Deanfield JE, Daiber A. Effects of tobacco cigarettes, e-cigarettes, and waterpipe smoking on endothelial function and clinical outcomes. Eur Heart J. Nov 1, 2020;41(41):4057-4070. [CrossRef] [Medline]
    47. Cheng X, Ferino E, Hull H, et al. Smoking affects gene expression in blood of patients with ischemic stroke. Ann Clin Transl Neurol. Sep 2019;6(9):1748-1756. [CrossRef] [Medline]
    48. Barua RS, Ambrose JA. Mechanisms of coronary thrombosis in cigarette smoke exposure. Arterioscler Thromb Vasc Biol. Jul 2013;33(7):1460-1467. [CrossRef] [Medline]
    49. Siasos G, Tsigkou V, Kokkou E, et al. Smoking and atherosclerosis: mechanisms of disease and new therapeutic approaches. Curr Med Chem. 2014;21(34):3936-3948. [CrossRef] [Medline]
    50. Tsai MC, Yeh TL, Hsu HY, et al. Comparison of four healthy lifestyle scores for predicting cardiovascular events in a national cohort study. Sci Rep. Nov 12, 2021;11(1):22146. [CrossRef] [Medline]
    51. Nang EE, Gitau Ngunjiri SA, Wu Y, et al. Validity of the International Physical Activity Questionnaire and the Singapore Prospective Study Program physical activity questionnaire in a multiethnic urban Asian population. BMC Med Res Methodol. Oct 13, 2011;11:141. [CrossRef] [Medline]
    52. Data and samples request. Singapore Population Health Studies. URL: https://blog.nus.edu.sg/sphs/data-and-samples-request/ [Accessed 2026-10-20]

    ‎
    AHEI: Alternative Healthy Eating Index
    AMI: acute myocardial infarction
    CVD: cardiovascular disease
    FFQ: food frequency questionnaire
    HR: hazard ratio
    MEC1: Multi-Ethnic Cohort Phase 1
    MET: metabolic equivalent of task
    SCCS–MEC: Singapore Consortium of Cohort Studies–Multi-Ethnic Cohort
    SF-36v2: 36-item Short Form Health Survey version 2
    STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

    Edited by Amaryllis Mavragani, Travis Sanchez; submitted 12.Mar.2026; peer-reviewed by Junqing Xie, Li-Hsuan Wang; final revised version received 20.Jul.2026; accepted 05.Aug.2026; published 11.Nov.2026.

    Copyright

    © Su Hyun Park, Peh Joo Ho, Xuan Liang, Rob van Dam, Mary F-F Chong, Charlie Guan Yi Lim, Xueling Sim, Airu Chia. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 11.Nov.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.