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Factors associated with eating behaviors in older adults from a socioecological model perspective

BMC Public Health volume 23, Article number: 1726 (2023) Cite this article

Abstract

Background

The eating behaviors of older adults are associated with multiple factors. To promote older adults’ healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults’ eating behaviors from a socioecological model (SEM) perspective.

Methods

In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients (β) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model.

Results

The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71–80 (β = -0.61), ≥ 81 (β = -1.12)], conscientiousness personality trait (β = -0.27), and higher family health levels (β = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school (β = 1.03), junior college and above (β = 1.71)], higher general self-efficacy (β = 0.09), more severe depression symptoms (β = 0.24), and employment (β = 0.82) tended to have poorer eating behaviors.

Conclusions

This study identified factors that are specifically associated with older adults’ eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults.

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Introduction

Globally the population is aging at an unprecedented rate. By 2050, the global population aged 60 and older will double from 1.4 billion in 2015 to 2.1 billion [1]. The World Health Organization proposed that it is very important to promote the health of the older adult population in response to aging [2]. The trend of population aging has increased people’s interest in promoting the constituents of healthy aging.

One of the fundamental aspects of healthy aging is healthy eating behaviors [3]. The findings of epidemiological studies have shown that maintaining healthy eating behaviors could reduce the incidence of morbidity, particularly associated with cognitive decline and metabolic disease, thus reducing the cost of healthcare [4, 5]. Additionally, some studies have also indicated that healthy eating behaviors are associated with better health and quality of life for older adults [6, 7]. However, older adults may eat unhealthily due to physical problems or decreased appetite [8], putting them at risk for malnutrition [9]. The effects of malnutrition can be severe on the health, well-being, and autonomy of older adults [10, 11]. Therefore, it is crucial to identify the associated factors of older adults’ eating behaviors, with the aim of developing specific health promotion strategies tailored to the needs of this targeted group.

Several studies have examined the associated factors of older adults’ eating behaviors [12,13,14]. A study has revealed that older adults eating behaviors are associated with multiple factors, mainly at the individual level (e.g., income) [15]. Several other studies highlighted the role of social factors in older adults’ eating behaviors, such as social relationships and social support [16, 17]. In addition, older adults eating habits have also been demonstrated to be associated with family factors, for example, whether living alone [18, 19]. However, it should be recognized that most existing studies on factors associated with eating behavior in older adults tended to focus on only one or a few angles (e.g., individual level and social level), a holistic view of factors associated with older adults eating behavior is lacking, thus, a comprehensive framework should be established to sort out associated factors. Additionally, based on the bio-psycho-social medical model, the person is regarded as a whole and is affected by environmental, physiological, psychological, and social factors, it is very necessary to study the associated factors of eating behavior from a holistic point of view. Thus, this study introduced the socioecological model (SEM) to assist in an understanding of factors associated with older adults eating behaviors at multiple levels [20].

SEM is a useful tool for addressing health behaviors by the attribution of health outcomes to factors. SEM can provide a theoretical framework for understanding the interactions between individual and environmental factors that affect health outcomes and behaviors [21]. In this multilevel model, individual characteristics, individual behaviors, interpersonal networks, community, and policy levels are all considered factors that relate to health outcomes and behaviors and highlight the importance of taking into consideration these factors [22]. SEM has been widely applied in the study of health behaviors. For instance, Wang et al. [23] employed SEM to investigate the associated factors with willingness of using mobile health devices and obtained some comprehensive insight into these factors. A study by Zhang et al. [24] examined the factors associated with the utilization of online medical services based on SEM, and valuable insights were also drawn.

Therefore, the objective of this study is to evaluate multilevel factors associated with elderly eating behaviors from the SEM perspective.

Methods

Survey design and participants

From 10 to 2021 to 15 September 2021, we conducted a survey using a multistage sampling method from 31 (91% of the total) provinces/autonomous regions/municipalities in mainland China. The random number table method was used to select 120 cities, including the capital and two to six prefectural cities in each province and autonomous region. Based on the Chinese population pyramid, quota sampling was performed on the selected residents from 120 cities (the quota attributes are sex, age, and urban-rural distribution), ensuring the above variables’ distribution of the obtained samples was basically in line with the population feature. Participants were surveyed via the Wenjuanxing platform by investigators issuing participants one-on-one questionnaires. Participants who were able to think but were not sufficiently actionable to fill in the questionnaire were interviewed one-on-one by the investigators and provided assistance without intervention.

All participants provided informed consent before data collection, and all data were kept strictly confidential. This study obtained ethics approval from the Ethics Committee of Jinan University (No. JNUKY-2021-018). All methods in our study were performed following the guidelines and regulations of the Declaration of Helsinki.

Sample size estimation

We calculated the sample size for a cross-sectional survey. The formula (N = Zα 2 × p × q/(d2) [25]) estimated a sample size of 1068. The α of this formula represented the significance level, Zα represented the statistic of the significance test, p × q represented the maximum estimation of variance, and d represented the permissible error.

Survey instruments

The study questionnaires were composed of self-designed questionnaires and a series of standardized questionnaires focusing on the factors associated with older adults’ eating behaviors (Fig. 1).

Fig. 1
figure 1

Factors associated with eating behaviors assembled based on the social-ecological model

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Self-designed questionnaires

The self-designed questionnaires were a combination of general demographic characteristics (i.e., age, sex, education level, diagnosis of chronic diseases, body mass index (BMI), smoking and drinking status, marital status, living alone, career status, urban-rural distribution, and medical insurance type) and some basic family information (i.e., number of siblings, number of house properties, and household per capita monthly income).

Standardized questionnaires

Eating behavior

The Eating Behavior Scale-Short Form (EBS-SF) was extracted from the 30-item Sakata Eating Behavior Scale (EBS) to evaluate participants’ dietary behaviors [26] and had good detection performance [27]. The scale consists of 7 items, including eating rhythm abnormalities, the feeling of satiety, eating habits, cognition of constitution, meal content, substitute eating and drinking, and motivation to eat. Each item is scored on a 4-point Likert scale, rated from 1 (strongly disagree) to 4 (strongly agree). Total summed EBS-SF scores range from 7 to 28, with higher scores reflecting worse eating behaviors. In this study, Cronbach’s α of the EBS-SF was 0.885.

Personality traits

The Big Five Inventory-10 (BFI-10) is used to evaluate participants’ personality traits [28]. There are five dimensions of personality in the BFI-10: extraversion, agreeableness, conscientiousness, neuroticism, and openness. Each personality dimension is measured by two items. And each item is scored on a 5-point Likert scale, rated from 1 (totally disagree) to 5 (totally agree). Reverse questions are scored from 1 (totally agree) to 5 (totally disagree). Higher individual dimension scores reflect greater levels of personality traits. As only two items per dimension are present in the BFI-10, no Cronbach’s α was calculated [29].

Self-efficacy

The New General Self-Efficacy Scale (NGSES) is used to evaluate participants’ perceived self-efficacy [30]. Each item is scored on a 5-point Likert scale, rated from 1 (strongly disagree) to 5 (strongly agree). Total summed NGSES scores range from 8 to 40, with higher scores reflecting greater self-efficacy. In this study, Cronbach’s α of the NGSES was 0.936.

Anxiety

The Generalized Anxiety Disorder-7 (GAD-7) is used to evaluate participants’ generalized anxiety symptoms [31]. Each item is scored on a 4-point Likert scale, rated from 0 (never) to 3 (nearly every day). Total summed GAD-7 scores range from 0 to 21, with higher scores reflecting more severe levels of anxiety symptoms. In this study, Cronbach’s α of the GAD-7 was 0.951.

Depression

The Patient Health Questionnaire-9 (PHQ-9) is used to evaluate participants’ depression symptoms [32]. Each item is scored on a 4-point Likert scale, rated from 0 (never) to 3 (nearly every day). Total summed PHQ-9 scores range from 0 to 27, with higher scores reflecting more severe levels of depression symptoms. In this study, Cronbach’s α of the PHQ-9 was 0.930.

Perceived social support

The Perceived Social Support Scale (PSSS) is used to evaluate participants’ perceived social support from family, friends, and significant others [33]. Each item is scored on a 7-point Likert scale, rated from 1 (strongly disagree) to 7 (strongly agree). Total summed PSSS scores range from 12 to 84, with higher scores reflecting a higher level of perceived social support. In this study, Cronbach’s α of the PSSS was 0.952.

Family health

The Family Health Scale-Short Form (FHS-SF) is used to evaluate participants’ family health environments [34]. Each item is scored on a 5-point Likert scale, rated from 1 (strongly disagree) to 5 (strongly agree). Reverse questions are reverse-scored. Total summed FHS-SF scores range from 10 to 50, with higher scores reflecting a higher level of family health. In this study, Cronbach’s α of the FHS-SF was 0.848.

Statistical methods

First, the Kolmogorov–Smirnov test was conducted to examine whether continuous variables followed a normal distribution. Continuous variables had a nonnormal distribution and were displayed as the median [interquartile range (IQR)]. Categorical variables were displayed as numbers (percentages). Second, the associations between the study variables and eating behaviors were measured using a univariate generalized linear model. Third, we examined the potential multicollinearity issue by calculating the variance inflation factor (VIF). The multicollinearity test demonstrated no collinearity among the independent variables in this study (maximum VIF = 3.5). Fourth, significant variables from the univariate generalized linear models (with P < 0.05) were investigated in multivariable generalized linear models. All two-sided P values < 0.05 were considered statistically significant. All statistical analyses were conducted using SPSS 19.0 (SPSS Inc., Chicago, IL, USA).

Results

Participant characteristics

A total of 11,031 questionnaires were initially collected, and after excluding 9884 questionnaire participants aged < 60 years old, the remaining 1147 questionnaires were included. In this study, 50.65% of participants were male, 61.90% had been diagnosed with chronic disease, 12.82% lived alone, and 42.98% lived in rural areas. The median score for the participants’ eating behavior scale was 14 points, self-efficacy was 28 points, anxiety was 2 points, depression was 4 points, perceived social support was 60 points, and family health was 38 points (Table 1).

Table 1 Participants characteristics based on the social-ecological model (n = 1147)
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Association between study variables and eating behaviors

The univariate generalized linear model showed that most variables in this study were associated with eating behaviors (P < 0.05). For example, individuals with older age [aged 71–80 (β = -1.19), ≥ 81 (β = -1.15)], extraversion (β = -0.19), agreeableness (β = -0.79), conscientiousness personality trait (β = -0.91) were more likely to have healthy eating behaviors. Individuals with higher education levels [junior high school and high school (β = 0.84), junior college and above (β = 1.80)], neuroticism personality trait (β = 0.62), higher levels of anxiety (β = 0.36) and depression (β = 0.35) were likely to have unhealthy eating behaviors (Table 2). The results from the multivariate generalized linear model indicated that participants with older age [aged 71–80 (β = -0.61), ≥ 81 (β = -1.12)], conscientiousness personality trait of BFI-10 (β = -0.27), and higher family health levels (β = -0.23) were inclined to have better eating behaviors. The participants with higher education levels [junior high school and high school (β = 1.03), junior college and above (β = 1.71)], had higher general self-efficacy (β = 0.09), had more severe depression symptoms (β = 0.24), and were employed (β = 0.82) tended to have worse eating behaviors (Table 3).

Table 2 Univariate generalized linear model analysis of associations between study variables and eating behaviors (n = 1147)
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Table 3 Multivariate generalized linear model analysis of associations between study variables and eating behaviors (n = 1147)
Full size table

Discussion

The current study examined the factors associated with eating behaviors in older adults based on SEM. The results showed that older age, higher educational level, conscientiousness personality trait, higher self-efficacy at the individual characteristics level; more severe depression symptoms at the individual behaviors level; higher family health at the interpersonal networks level; and employed career status at community level were associated with eating behaviors in older adults.

This study showed that SEM can help to better sort out and study the factors associated with older adults eating behaviors from a relatively comprehensive perspective. The results indicated that the factors associated with the eating behaviors of older adults are mainly concentrated in the SEM level of individual characteristics, and second, the level of individual behaviors, interpersonal networks, and community levels have also been found to have relevant factors. It is worth carrying out in-depth analysis and corresponding intervention guidance for improving older adults’ healthy eating behaviors from these perspectives in the future.

Our study revealed that older age was associated with healthy eating behaviors, which was in line with previous studies [35, 36]. The evidence suggested that aging was positively associated with healthy eating behaviors [37]. Older adults tend to maintain healthy eating behaviors, including consuming more fresh vegetables and fruits rather than high-fat foods [38]. With increasing age, older adults have gradually become aware of aging and the decline of digestive system functions [39], they may thus pay more attention to their health, such as health preservation, and diet. Additionally, in China, there is a high prevalence of digestive system illnesses in older adults, such as gastritis and colitis [40, 41], as well as a high prevalence of hypertension, diabetes, and hyperlipidemia [42, 43]. To comply with the need for treatment and care for digestive system diseases and other chronic diseases, many older adults tend to adopt healthy eating practices, such as eating soft food, low-salt and low-fat meals, having more meals a day but less food at each, etc. [6, 44]. Generally, older adults are likely to have more spare time after retirement, they thus have more time and energy to cook healthier foods, so they consume fewer unhealthy foods.

In this study, we found that the conscientiousness personality trait rated by BFI-10 was associated with more healthful eating behaviors, this finding was consistent with other studies among older adults [45, 46]. Individuals with high conscientiousness appear to maintain restrained regulatory eating and practice less counterregulatory emotional or external eating [47], and may be more capable of avoiding or controlling emotional stressors, resulting in decreased emotional eating and further decreased unhealthy eating [48]. Additionally, individuals with high conscientiousness may have more self-control resources to maintain their diet goals in tempting food situations and inhibit their eating enjoyment goals, thus adhering to a balanced and healthy diet [49]. As individuals generally pay more attention to their health in old age. Older adults should be encouraged to pay conscientious attention to diet. This highlights the need for personality traits that should be considered in the promotion of healthy eating among older adults.

The present study revealed an intriguing finding whereby a positive association was observed between a high education level and unhealthy eating behaviors among older adults. This finding contrasted with the expected trend observed in prior studies wherein individuals with higher education tend to exhibit healthier dietary habits [50, 51]. One potential explanation for this intriguing phenomenon lies in the possibility of overconfidence among individuals with elevated educational backgrounds when applying nutritional knowledge, resulting in suboptimal dietary choices. This phenomenon finds support in the research, which suggested that those with higher education levels might be more susceptible to specific nutritional information, potentially deviating from a comprehensive dietary approach [52]. Additionally, individuals with higher education may experience increased societal pressures, demanding a delicate balance between their professional, social, and familial responsibilities during their tenure [53]. These pressures could lead to decisions favoring expedient and convenient food choices, potentially at the expense of adhering to the principles of a wholesome diet [54]. This supposition resonates with the findings of Putri et al., which emphasized that individuals experiencing elevated stress levels tend to opt for high-calorie, low-nutrient foods [55]. These dietary habits tend to become ingrained from a young age and persist into later life, forming enduring patterns [56]. Furthermore, the interplay of social identity and cultural factors might wield influence over this observed association. Notably, individuals with advanced educational backgrounds may possess distinct social identities and cultural values that impact their dietary preferences [57]. Specific societal subgroups might exhibit a preference for traditional high-sugar, high-fat foods, thereby conflicting with the recommendations of a health-conscious diet [58]. The implications of this noteworthy revelation underscore the importance of addressing potential determinants contributing to unhealthy dietary behaviors among older adults with elevated educational status. Consequently, the development of targeted and effective public health interventions tailored to this specific demographic becomes imperative. Given the ongoing global aging phenomenon, promoting healthful dietary habits, and tackling the unique challenges faced by older adults with higher educational backgrounds emerge as essential endeavors for optimizing both their health and overall quality of life.

This study also confirmed that high self-efficacy was associated with worse eating behaviors among older adults. In contrast to prior research, which has suggested that older adults with high self-efficacy exhibit better coping abilities in challenging situations, display goal-oriented behaviors, and are more likely to adopt healthy eating habits due to the significance of overcoming failures and making persistent efforts to maintain healthy dietary patterns [59, 60]. The underlying reason for the observed association between high self-efficacy and worse eating behaviors among older adults could be attributed to the complexity of psychological factors associated with eating behaviors in this population. First, it is crucial to consider the impact of self-regulation abilities. Despite having high self-efficacy, individuals may face hindrances in regulating food intake due to factors like emotional eating tendencies or external food-related cues, resulting in self-efficacy not always translating into optimal eating behaviors [61, 62]. Second, the role of habit formation and automaticity in eating behaviors should not be overlooked. Prior research has highlighted that habits play a significant role in determining eating patterns, especially among older adults who may have established long-standing routines [63]. Consequently, even with high self-efficacy, individuals may experience overshadowed confidence in making consistently healthy choices if unhealthy eating habits have become ingrained over time [64]. Furthermore, the dietary decisions of older adults are frequently shaped by their social networks, familial interactions, and the accessibility of healthful food alternatives within their immediate surroundings [65]. As a result, even those individuals possessing elevated self-efficacy could yield to external influences or contextual limitations, potentially predisposing them towards favoring less nutritious dietary selections [66]. Therefore, while self-efficacy remains a crucial determinant, its association can be intricately interwoven with external factors, necessitating a comprehensive understanding of the multifaceted dynamics that underlie the dietary behaviors of older adults.

Our study discovered that higher family health levels were associated with healthy eating behaviors. It has been demonstrated that the family environment can have a significant impact on health behaviors [67]. Previous studies found that a positive family system can contribute to establishing and promoting beneficial health behaviors through the exemplary role, provision of healthy foods, and encouragement of healthy eating behaviors [68]. The Chinese government has also realized the necessity of improving the health of the whole population and proposed the Healthy China Plan in 2016, with the aim of promoting people’s health and then improving the level of national health literacy [69], which further improves the level of family health literacy to a certain extent. The family is the tiniest cell of society and plays a vital role in both individual life and society. This study found that the health of individuals cannot be separated from the health of the family, and it is very important to implement the guidance of the national health environment into specific family life. It is thus clear that policymakers who promote healthy eating behaviors should give full play to the positive role of family health resources in establishing and promoting good eating behaviors.

We also found that more severe depression symptoms were associated with worse eating behaviors, which was consistent with prior research [70, 71]. Individuals with depression symptoms are more likely to increase unhealthy food consumption [72] and have unhealthy eating habits [73]. Furthermore, depression affects eating motivations and drives food choices toward less healthy foods [74]. There was, however, approximately 40% of older adults aged 60 or older in China who reported having depression symptoms [75]. It is therefore necessary to focus on depressed older adults. While considering addressing the psychological problems of older adults, lifestyle problems such as diet should also be considered to promote the improvement of their overall health.

Moreover, our study revealed that the career status of employed was associated with unhealthy eating behaviors. Employed older adults may put in more physical and mental effort to complete the work, which is very challenging for physical strength, time, and energy [76]. Therefore, older adults in employment can crowd out some of the time that could be divided between food preparation, learning about dietary management, etc. Additionally, numerous studies have confirmed that work-related stress is associated with unhealthy eating behaviors [77, 78]. Compared to other working-age populations, older adults are at significantly higher risk of stress because of their diminished physical and mental abilities [79, 80]. Therefore, it is necessary to pay attention to the dietary problems of employed older adults.

Several potential limitations of this study should be acknowledged. First, due to the cross-sectional study design, we cannot infer causal implications. Second, there is the possibility that data may be to some extent biased since some information was self-reported. Third, we could not exclude participants who were on a weight-loss diet, so careful consideration should be given to the possibility of measurement bias, as well as continued evaluation of the EBS-SF. Fourth, although some variables (i.e., age, sex, and urban-rural distribution) had been quota, there may have been some uneven data distribution (e.g., education level, smoking, and drinking), which could have a certain effect on the outcomes. Fifth, the data collection in this study was during the COVID-19 epidemic and sometimes was subject to epidemic containment management, which may affect eating behaviors. Sixth, although we have considered multiple factors related to eating behaviors in older adults from a socioecological model perspective, this study lacks the inclusion of nutritional status and cognitive functioning as variables. Nutritional status is significantly associated with eating behaviors in older adults, while cognitive functioning also plays a crucial role in their dietary decision-making and habits. Therefore, the omission of these variables may have impacted a comprehensive understanding of eating behaviors in older adults. Future research should incorporate measures of nutritional status and cognitive functioning to gain a more comprehensive insight into the factors associated with eating behaviors among older adults.

Conclusions

This study identified factors associated with older adults eating behaviors and provided an in-depth understanding of these factors at five levels based on SEM. The findings provide information about integral assessments of older adults’ eating behaviors and may contribute to the development of targeted and multipronged health-promoting strategies to improve the health of older adults. Additionally, strategies that promote healthier eating behaviors in older adults from a holistic perspective may contribute to healthy aging through better nutrition.

Availability of data and materials

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

References

  1. United Nations: World Population Ageing: 1950–2050. New York: United Nations; 2002.

  2. Lloyd-Sherlock P, Kalache A, Kirkwood T, et al. Who’s proposal for a decade of healthy ageing. Lancet. 2019;394(10215):2152–3. https://doi.org/10.1016/s0140-6736(19)32522-x.

    Article  PubMed  Google Scholar 

  3. Sadana R, Blas E, Budhwani S, et al. Healthy ageing: raising awareness of inequalities, determinants, and what could be done to improve health equity. Gerontologist. 2016;56(Suppl 2):178–93. https://doi.org/10.1093/geront/gnw034.

    Article  Google Scholar 

  4. Lesáková D. Health perception and food choice factors in predicting healthy consumption among elderly. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 2018;66(6):1527–34.

    Article  Google Scholar 

  5. Robinson SM. Improving nutrition to support healthy ageing: what are the opportunities for intervention? Proc Nutr Soc. 2018;77(3):257–64. https://doi.org/10.1017/s0029665117004037.

    Article  PubMed  Google Scholar 

  6. Govindaraju T, Sahle BW, McCaffrey TA, et al. Dietary patterns and quality of life in older adults: a systematic review. Nutrients. 2018;10(8):971.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Zaragoza-Martí A, Ferrer-Cascales R, Hurtado-Sánchez JA, et al. Relationship between adherence to the mediterranean diet and health-related quality of life and life satisfaction among older adults. J Nutr Health Aging. 2018;22(1):89–96. https://doi.org/10.1007/s12603-017-0923-2.

    Article  CAS  PubMed  Google Scholar 

  8. Drewnowski A, Shultz JM. Impact of aging on eating behaviors, food choices, nutrition, and health status. J Nutr Health Aging. 2001;5(2):75–9.

    CAS  PubMed  Google Scholar 

  9. Fávaro-Moreira NC, Krausch-Hofmann S, Matthys C, et al. Risk factors for malnutrition in older adults: a systematic review of the literature based on longitudinal data. Adv Nutr. 2016;7(3):507–22. https://doi.org/10.3945/an.115.011254.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Leslie W. Improving the dietary intake of frail older people. Proceedings of the Nutrition Society. 2011;70(2):263–7.

    Article  CAS  PubMed  Google Scholar 

  11. Di Francesco V, Fantin F, Omizzolo F, et al. The anorexia of aging. Dig Dis. 2007;25(2):129–37. https://doi.org/10.1159/000099477.

    Article  PubMed  Google Scholar 

  12. Yannakoulia M, Mamalaki E, Anastasiou CA, et al. Eating habits and behaviors of older people: where are we now and where should we go? Maturitas. 2018;114:14–21. https://doi.org/10.1016/j.maturitas.2018.05.001.

    Article  PubMed  Google Scholar 

  13. Stosovic D, Vasiljevic N, Jovanovic V, et al. Dietary habits of older adults in serbia: findings from the national health survey. Front Public Health. 2021;9: 610873. https://doi.org/10.3389/fpubh.2021.610873.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Payette H, Shatenstein B. Determinants of healthy eating in community-dwelling elderly people. Can J Public Health. 2005;96(Suppl 3):27–31 (s30-25).

    Google Scholar 

  15. Host A, McMahon AT, Walton K, et al. Factors influencing food choice for independently living older people-a systematic literature review. J Nutr Gerontol Geriatr. 2016;35(2):67–94. https://doi.org/10.1080/21551197.2016.1168760.

    Article  PubMed  Google Scholar 

  16. Vesnaver E, Keller HH. Social influences and eating behavior in later life: a review. J Nutr Gerontol Geriatr. 2011;30(1):2–23. https://doi.org/10.1080/01639366.2011.545038.

    Article  PubMed  Google Scholar 

  17. de Boer A, Ter Horst GJ, Lorist MM. Physiological and psychosocial age-related changes associated with reduced food intake in older persons. Ageing Res Rev. 2013;12(1):316–28. https://doi.org/10.1016/j.arr.2012.08.002.

    Article  PubMed  Google Scholar 

  18. Conklin AI, Maguire ER, Monsivais P. Economic determinants of diet in older adults: systematic review. J Epidemiol Community Health. 2013;67(9):721–7. https://doi.org/10.1136/jech-2013-202513.

    Article  PubMed  Google Scholar 

  19. Whitelock E, Ensaff H. On your own: older adults’ food choice and dietary habits. Nutrients. 2018;10(4): 413. https://doi.org/10.3390/nu10040413.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bronfenbrenner U. The ecology of human development experiments by nature and design. Cambridge: Harvard University Press; 1996.

  21. Golden SD, Earp JA. Social ecological approaches to individuals and their contexts: twenty years of health education & behavior health promotion interventions. Health Educ Behav. 2012;39(3):364–72. https://doi.org/10.1177/1090198111418634.

    Article  PubMed  Google Scholar 

  22. Sallis JF, Owen N, Fisher E. Ecological models of health behavior. Health Behavior: Theory Research and Practice. 2015;5:43–64.

    Google Scholar 

  23. Wang X, Wu Y, Meng Z, et al. Willingness to use mobile health devices in the post-covid-19 era: Nationwide cross-sectional study in china. J Med Internet Res. 2023;25: e44225. https://doi.org/10.2196/44225.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhang C, Peng XQ, Jiang YZ, et al. Online medical services utilization evaluated through the lens of socioecological theory and the information-motivation-behavioral skills model: evidence from china. Ann N Y Acad Sci. 2021;1500(1):82–92. https://doi.org/10.1111/nyas.14609.

    Article  PubMed  Google Scholar 

  25. Kotrlik J, Higgins C. Organizational research: Determining appropriate sample size in survey research appropriate sample size in survey research. Inf Technol Learn Perform J. 2001;19(1):43.

    Google Scholar 

  26. Sakata T. Treatment manual for morbid obesity. Tokyo: Ishiyaku Shuppan; 1996.

    Google Scholar 

  27. Tayama J, Ogawa S, Takeoka A, et al. Item response theory-based validation of a short form of the eating behavior scale for japanese adults. Med (Baltim). 2017;96(42):e8334. https://doi.org/10.1097/md.0000000000008334.

    Article  Google Scholar 

  28. Fink M, Bäuerle A, Schmidt K, et al. Covid-19-fear affects current safety behavior mediated by neuroticism-results of a large cross-sectional study in germany. Front Psychol. 2021;12: 671768. https://doi.org/10.3389/fpsyg.2021.671768.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Soto CJ, John OP. Short and extra-short forms of the big five inventory–2: the bfi-2-s and bfi-2-xs. J Res Pers. 2017;68:69–81. https://doi.org/10.1016/j.jrp.2017.02.004.

    Article  Google Scholar 

  30. Chen G, Gully SM, Eden D. Validation of a new general self-efficacy scale. Organizational Res Methods. 2001;4(1):62–83.

    Article  CAS  Google Scholar 

  31. Spitzer RL, Kroenke K, Williams JB, et al. A brief measure for assessing generalized anxiety disorder: the gad-7. Arch Intern Med. 2006;166(10):1092–7. https://doi.org/10.1001/archinte.166.10.1092.

    Article  PubMed  Google Scholar 

  32. Kroenke K, Spitzer RL, Williams JB. The phq-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Li L, Peng T, Liu R, et al. Development of the psychosomatic symptom scale (psss) and assessment of its reliability and validity in general hospital patients in china. Gen Hosp Psychiatry. 2020;64:1–8. https://doi.org/10.1016/j.genhosppsych.2020.01.008.

    Article  PubMed  Google Scholar 

  34. Crandall A, Weiss-Laxer NS, Broadbent E, et al. The family health scale: reliability and validity of a short- and long-form. Front Public Health. 2020;8: 587125. https://doi.org/10.3389/fpubh.2020.587125.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chan YC, Suzuki M, Yamamoto S. Dietary, anthropometric, hematological and biochemical assessment of the nutritional status of centenarians and elderly people in okinawa, japan. J Am Coll Nutr. 1997;16(3):229–35. https://doi.org/10.1080/07315724.1997.10718679.

    Article  CAS  PubMed  Google Scholar 

  36. Gu Q, Sable CM, Brooks-Wilson A, et al. Dietary patterns in the healthy oldest old in the healthy aging study and the canadian longitudinal study of aging: a cohort study. BMC Geriatr. 2020;20(1):106. https://doi.org/10.1186/s12877-020-01507-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Aoun C, Nassar L, Soumi S, et al. The cognitive, behavioral, and emotional aspects of eating habits and association with impulsivity, chronotype, anxiety, and depression: a cross-sectional study. Front Behav Neurosci. 2019;13: 204. https://doi.org/10.3389/fnbeh.2019.00204.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Nicklett EJ, Kadell AR. Fruit and vegetable intake among older adults: a scoping review. Maturitas. 2013;75(4):305–12. https://doi.org/10.1016/j.maturitas.2013.05.005.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kojima K, Ogawa A, Nakamura R, et al. Effect of dietary medium-chain triacylglycerol on serum albumin and nitrogen balance in malnourished rats. J Clin Biochem Nutr. 2008;42(1):45–9. https://doi.org/10.3164/jcbn.2008007.

    Article  CAS  PubMed  Google Scholar 

  40. Yang L, Kartsonaki C, Yao P, et al. The relative and attributable risks of cardia and non-cardia gastric cancer associated with helicobacter pylori infection in china: a case-cohort study. Lancet Public Health. 2021;6(12):e888–896. https://doi.org/10.1016/s2468-2667(21)00164-x.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Shi HY, Chan FK, Leung WK, et al. Natural history of elderly-onset ulcerative colitis: results from a territory-wide inflammatory bowel disease registry. J Crohns Colitis. 2016;10(2):176–85. https://doi.org/10.1093/ecco-jcc/jjv194.

    Article  PubMed  Google Scholar 

  42. Wang HH, Wang JJ, Wong SY, et al. Epidemiology of multimorbidity in china and implications for the healthcare system: cross-sectional survey among 162,464 community household residents in southern china. BMC Med. 2014;12:188. https://doi.org/10.1186/s12916-014-0188-0.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Song PK, Man QQ, Li H, et al. Trends in lipids level and dyslipidemia among chinese adults, 2002–2015. Biomed Environ Sci. 2019;32(8):559–70. https://doi.org/10.3967/bes2019.074.

    Article  CAS  PubMed  Google Scholar 

  44. De Montez ÍR, Bergheim I, Brombach C. Beyond the individual -a scoping review and bibliometric mapping of ecological determinants of eating behavior in older adults. Public Health Rev. 2022;43:1604967. https://doi.org/10.3389/phrs.2022.1604967.

    Article  Google Scholar 

  45. Mõttus R, McNeill G, Jia X, et al. The associations between personality, diet and body mass index in older people. Health Psychol. 2013;32(4):353–60. https://doi.org/10.1037/a0025537.

    Article  PubMed  Google Scholar 

  46. Weston SJ, Edmonds GW, Hill PL. Personality traits predict dietary habits in middle-to-older adults. Psychol Health Med. 2020;25(3):379–87. https://doi.org/10.1080/13548506.2019.1687918.

    Article  PubMed  Google Scholar 

  47. Keller C, Siegrist M. Does personality influence eating styles and food choices? Direct and indirect effects. Appetite. 2015;84:128–38. https://doi.org/10.1016/j.appet.2014.10.003.

    Article  PubMed  Google Scholar 

  48. Shanahan MJ, Hill PL, Roberts BW, et al. Conscientiousness, health, and aging: the life course of personality model. Dev Psychol. 2014;50(5):1407–25. https://doi.org/10.1037/a0031130.

    Article  PubMed  Google Scholar 

  49. Stroebe W, van Koningsbruggen GM, Papies EK, et al. Why most dieters fail but some succeed: a goal conflict model of eating behavior. Psychol Rev. 2013;120(1):110–38. https://doi.org/10.1037/a0030849.

    Article  PubMed  Google Scholar 

  50. Wongprawmas R, Sogari G, Menozzi D, et al. Strategies to promote healthy eating among university students: a qualitative study using the nominal group technique. Front Nutr. 2022;9:821016. https://doi.org/10.3389/fnut.2022.821016.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Thorpe MG, Milte CM, Crawford D, et al. Education and lifestyle predict change in dietary patterns and diet quality of adults 55 years and over. Nutr J. 2019;18(1):67. https://doi.org/10.1186/s12937-019-0495-6.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of oecd countries for the period 1995–2015. Archives of Public Health. 2020;78(1):20. https://doi.org/10.1186/s13690-020-00402-5.

    Article  PubMed  PubMed Central  Google Scholar 

  53. S T SNG. Work-life balance -a systematic review. Vilakshan - XIMB Journal of Management. 2023;20(2):258–76. https://doi.org/10.1108/XJM-10-2020-0186.

    Article  Google Scholar 

  54. Firth J, Gangwisch JE, Borsini A, et al. Food and mood: how do diet and nutrition affect mental wellbeing? BMJ (Clinical Research ed). 2020;369:m2382. https://doi.org/10.1136/bmj.m2382.

    Article  PubMed  Google Scholar 

  55. Putri P. Association of high calorie food and coffee consumption pattern, sleep duration and stress level with nutritional status in final year students. Media Gizi Kesmas. 2022;11:464–74. https://doi.org/10.20473/mgk.v11i2.2022.464-474.

    Article  Google Scholar 

  56. Winpenny EM, van Sluijs EMF, White M, et al. Changes in diet through adolescence and early adulthood: longitudinal trajectories and association with key life transitions. Int J Behav Nutr Phys Activity. 2018;15(1):86. https://doi.org/10.1186/s12966-018-0719-8.

    Article  Google Scholar 

  57. Templeton E, Stanton M, Zaki J. Social norms shift preferences for healthy and unhealthy foods. PLoS ONE. 2016;11:e0166286. https://doi.org/10.1371/journal.pone.0166286.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Angeles-Agdeppa I, Sun Y, Tanda KV. Dietary pattern and nutrient intakes in association with non-communicable disease risk factors among filipino adults: a cross-sectional study. Nutr J. 2020;19(1):79. https://doi.org/10.1186/s12937-020-00597-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Dzerounian J, Pirrie M, AlShenaiber L, et al. Health knowledge and self-efficacy to make health behaviour changes: a survey of older adults living in ontario social housing. BMC Geriatr. 2022;22(1):473. https://doi.org/10.1186/s12877-022-03116-1.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Wu F, Sheng Y. Social support network, social support, self-efficacy, health-promoting behavior and healthy aging among older adults: a pathway analysis. Arch Gerontol Geriatr. 2019;85: 103934. https://doi.org/10.1016/j.archger.2019.103934.

    Article  PubMed  Google Scholar 

  61. Frayn M, Livshits S, Knäuper B. Emotional eating and weight regulation: a qualitative study of compensatory behaviors and concerns. J Eat Disorders. 2018;6(1):23. https://doi.org/10.1186/s40337-018-0210-6.

    Article  Google Scholar 

  62. Russell CG, Russell A. Food and non-food self-regulation in childhood: a review and reciprocal analysis. Int J Behav Nutr Phys Activity. 2020;17(1):33. https://doi.org/10.1186/s12966-020-00928-5.

    Article  Google Scholar 

  63. Fan H, Lee Y-H, Chang Y-C, et al. Associations of dietary habits and sleep in older adults: a 9-year follow-up cohort study. Eur Geriatr Med. 2021;12(1):123–31. https://doi.org/10.1007/s41999-020-00377-0.

    Article  PubMed  Google Scholar 

  64. Trapp G, Hickling S, Christian H, et al. Individual, social, and environmental correlates of healthy and unhealthy eating. Health Educ Behavior. 2015;42:759–68. https://doi.org/10.1177/1090198115578750.

    Article  Google Scholar 

  65. Asamane EA, Greig CA, Thompson JL. Social networks and their influences on nutrient intake, nutritional status and physical function in community-dwelling ethnically diverse older adults: a mixed-methods longitudinal study. BMC Public Health. 2020;20(1):1011. https://doi.org/10.1186/s12889-020-09153-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Yokoro M, Otaki N, Imamura T, et al. Association between social network and dietary variety among community-dwelling older adults. Public Health Nutr. 2023;1–22. https://doi.org/10.1017/S1368980023001325.

  67. Niermann CY, Kremers SP, Renner B, et al. Family health climate and adolescents’ physical activity and healthy eating: a cross-sectional study with mother-father-adolescent triads. PLoS ONE. 2015;10(11):e0143599. https://doi.org/10.1371/journal.pone.0143599.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Vandeweghe L, Moens E, Braet C, et al. Perceived effective and feasible strategies to promote healthy eating in young children: Focus groups with parents, family child care providers and daycare assistants. BMC Public Health. 2016;16(1):1045. https://doi.org/10.1186/s12889-016-3710-9.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Jiang Z, Jiang W. Health education in the healthy china initiative 2019–2030. China CDC Wkly. 2021;3(4):78–80. https://doi.org/10.46234/ccdcw2021.018.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Fox CK, Gross AC, Rudser KD, et al. Depression, anxiety, and severity of obesity in adolescents: is emotional eating the link? Clin Pediatr (Phila). 2016;55(12):1120–5. https://doi.org/10.1177/0009922815615825.

    Article  PubMed  Google Scholar 

  71. Devonport TJ, Nicholls W, Fullerton C. A systematic review of the association between emotions and eating behaviour in normal and overweight adult populations. J Health Psychol. 2019;24(1):3–24. https://doi.org/10.1177/1359105317697813.

    Article  PubMed  Google Scholar 

  72. Salazar-Fernández C, Palet D, Haeger PA, et al. Covid-19 perceived impact and psychological variables as predictors of unhealthy food and alcohol consumption trajectories: the role of gender and living with children as moderators. Int J Environ Res Public Health. 2021;18(9): 4542. https://doi.org/10.3390/ijerph18094542.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wang X, Wu Y, Shi X, et al. Associations of lifestyle with mental health and well-being in chinese adults: a nationwide study. Front Nutr. 2023;10:10. https://doi.org/10.3389/fnut.2023.1198796.

    Article  CAS  Google Scholar 

  74. Ramón-Arbués E, Martínez Abadía B, Granada López JM, et al. [eating behavior and relationships with stress, anxiety, depression and insomnia in university students]. Nutr Hosp. 2019;36(6):1339–45. https://doi.org/10.20960/nh.02641.

    Article  PubMed  Google Scholar 

  75. Ni Y, Tein JY, Zhang M, et al. Changes in depression among older adults in china: a latent transition analysis. J Affect Disord. 2017;209:3–9. https://doi.org/10.1016/j.jad.2016.11.004.

    Article  PubMed  Google Scholar 

  76. Ćwirlej-Sozańska A, Widelak M, Wiernasz M, et al. An assessment of the work ability, disability and quality of life of working people of pre-retirement and retirement age in poland - a cross-sectional pilot study. Int J Occup Med Environ Health. 2021;34(1):69–85. https://doi.org/10.13075/ijomeh.1896.01591.

    Article  PubMed  Google Scholar 

  77. Liu Y, Song Y, Koopmann J, et al. Eating your feelings? Testing a model of employees’ work-related stressors, sleep quality, and unhealthy eating. J Appl Psychol. 2017;102(8):1237–58. https://doi.org/10.1037/apl0000209.

    Article  PubMed  Google Scholar 

  78. Hellerstedt WL, Jeffery RW. The association of job strain and health behaviours in men and women. Int J Epidemiol. 1997;26(3):575–83. https://doi.org/10.1093/ije/26.3.575.

    Article  CAS  PubMed  Google Scholar 

  79. Stolz E, Mayerl H, Waxenegger A, et al. Explaining the impact of poverty on old-age frailty in europe: material, psychosocial and behavioural factors. Eur J Public Health. 2017;27(6):1003–9. https://doi.org/10.1093/eurpub/ckx079.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Levy H. Income, poverty, and material hardship among older americans. Rsf. 2015;1(1):55–77. https://doi.org/10.7758/rsf.2015.1.1.04.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

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Funding

This research was funded by the Social Science Fund Project Planning of Liaoning Province (No. L20BGL059).

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  1. Xue Wang and Yibo Wu share the first authorship on this work.

Authors and Affiliations

  1. Department of Community Nursing, School of Nursing, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, 110122, China

    Xue Wang, Juanxia Miao, Keping Pu & Shuang Zang

  2. School of Public Health, Peking University, No.38 Xueyuan Road, Haidian District, Beijing, 100191, China

    Yibo Wu

  3. Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, To Yuen Building, No.31 To Yuen Street, Hong Kong, 999077, China

    Wai-Kit Ming

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Contributions

Xue Wang and Yibo Wu shared the first authorship on this work. Conceptualization: Xue Wang and Shuang Zang. Methodology: Xue Wang and Yibo Wu. Data validation and analyses: Xue Wang and Yibo Wu. Data interpretation: All authors. Writing—original draft preparation: Xue Wang and Yibo Wu. Writing—review and editing: All authors. Supervision: All authors. Final approval of manuscript: All authors.

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Correspondence to Shuang Zang.

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This study obtained ethics approval from the Ethics Committee of Jinan University (No. JNUKY-2021-018). All participants provided informed consent before data collection, and all data were kept strictly confidential. All methods in our study were performed following the guidelines and regulations of the Declaration of Helsinki.

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Wang, X., Wu, Y., Miao, J. et al. Factors associated with eating behaviors in older adults from a socioecological model perspective. BMC Public Health 23, 1726 (2023). https://doi.org/10.1186/s12889-023-16651-2

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ISSN: 1471-2458

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