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Article

Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging

by
Christine Marie Mills
1,* and
Catherine Donnelly
2
1
College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
2
School of Rehabilitation Therapy and Health Services and Policy Research Institute, Queen’s University, Kingston, ON K7L 3N6, Canada
*
Author to whom correspondence should be addressed.
J. Ageing Longev. 2026, 6(1), 4; https://doi.org/10.3390/jal6010004 (registering DOI)
Submission received: 6 October 2025 / Revised: 25 November 2025 / Accepted: 17 December 2025 / Published: 30 December 2025
Versions Notes

Abstract

Nutrition risk is prevalent in community-dwelling older adults, and leads to increased morbidity and mortality. Understanding the factors associated with the development of high nutrition risk is crucial for the development of appropriate programs and policies to address this problem. Therefore, our objective was to identify the psychosocial factors correlated with the development of high nutrition risk, as assessed by SCREEN-8, among Canadian adults categorized by ten-year age groups (45–54, 55–64, 65–74, and 75+). We used data from 17,051 participants in the tracking cohort of the Canadian Longitudinal Study on Aging and employed multivariable binomial logistic regression to identify the social and demographic factors associated with the emergence of high nutrition risk at follow-up, three years after the baseline. Baseline data were gathered between 2011 and 2015. At baseline, 34.4% of participants across all age groups were at high nutrition risk, while 40.0% were at high risk at follow-up. Factors consistently associated with the development of high nutrition risk across all age groups included lower levels of social support, lower self-rated social standing, infrequent participation in sports or physical activities, infrequent participation in cultural or educational activities, and lower household incomes. Programs and policies addressing these factors may reduce the prevalence of high nutrition risk and the development of high nutrition risk.

1. Introduction

Older adults represent the fastest-growing demographic group in Canada [1] and worldwide [2,3]. These aging populations present a pressing challenge for health systems and communities [4,5]. Most older adults want to age in place in their homes and within their communities [6]. An older adult is able to age in place when their health, access to services, and social support interact in a manner that facilitates their ability to live safely and independently in their homes or communities for as long as they wish [7]. Although most older adults desire to age in place, this is challenging for many. Healthy lifestyles, including an adequate diet, are essential for aging in place [8], and nutrition is one of the few modifiable risk factors for health as we age [8,9].
Eating habits change with age due to physiological, social, and psychological changes that affect eating habits and nutrient intake. Such changes may include new health conditions, loss of a driver’s license, or bereavement [10,11]. Inadequate dietary intake can lead to high nutrition risk. While there is no single definition of nutrition risk that is agreed upon, it “represents the determinants and risk factors that place an individual at risk for poor food intake and, if not interrupted, can lead to malnutrition” [12]. Individuals at high nutrition risk have a higher chance of being admitted to the hospital and an increased risk of mortality, even when socioeconomic status, health status, and health behaviors are considered [13,14]. Nutrition risk is also associated with poor quality of life [8,15], early institutionalization [9], and frailty [16]. Despite its significance, nutrition risk remains under-recognized in public health and clinical settings [17,18].
In the community, nutrition risk is typically measured using questionnaires that elicit information on dietary intake, food habits, and weight changes in older adults [19]. In Canada, nationally representative studies have found that approximately one-third of community-living adults aged 45 years and older are at high nutrition risk [20,21]. Nutrition risk is also highly prevalent in other countries, with one study finding that 61.5% of older adults in the Netherlands, 68.2% of older adults in New Zealand, and 70.1% of older adults in Canada were at high risk [22]. Regarding malnutrition, a study that synthesized 24 datasets from 12 countries found that 13.8% of older adults who dwell in the community were malnourished [23]. A similar prevalence (10.5%) was found in 15 samples based on data from 11 high-income European countries [24]. The differing percentages of nutrition risk are due to different tools, cut-offs to identify at-risk individuals, and/or different subsets of the population being assessed [22]. Despite these differences, nutrition risk is prevalent and a growing concern for the aging population in Canada.
Research has uncovered numerous social factors associated with the prevalence of high nutrition risk in older adults [25,26]. These include social support, engagement, living situation (alone or with others), and participation [21,27]. It is widely recognized that dining with others enhances dietary intake, leading to reduced nutrition risk [25], while eating alone is linked to high nutrition risk [25,28]. Living alone has also been associated with nutrition risk in multiple studies [29,30,31]. Some studies have indicated that social isolation significantly contributes to increased nutrition risk [14,21]. Social relationships may mitigate nutrition risk by promoting adherence to dietary norms, while eating with others may provide “social cues for when and what to eat” [25] (p.15). An individual’s social support network may also encourage healthy behaviors, such as including sufficient amounts of nutrient-dense foods in the diet [32]. Studies have found that lower levels of social support are associated with an increased prevalence of high nutrition risk [32]. Individuals possessing higher levels of social support may receive more help with food-related tasks, such as preparing and cooking meals and acquiring food [25], which helps to reduce nutrition risk [33].
Despite the recognized effect of social factors on nutrition risk, most studies in this area have not used a theoretical framework to examine how social factors and nutrition risk interrelate; therefore, the variables chosen in many previous studies have not been theoretically justified for their inclusion. Additionally, few studies have examined how the factors associated with nutrition risk differ between age groups and, due to changes related to aging, these factors may differ between younger and older groups. Most research to date has also used cross-sectional and small sample data, which limits our understanding of the social determinants that are most relevant for targeting interventions [34]. It is essential to understand the social factors associated with nutrition risk in different age groups so that appropriate interventions, policies, and programs can be designed and implemented to reduce nutrition risk and its negative consequences. Reducing the prevalence of high nutrition risk will therefore help older adults to age in place in their homes or communities.
Therefore, our research aims to address critical gaps in the literature by applying Berkman’s social network theory [35] to examine the psychosocial determinants of nutrition risk across age groups. Using data from the Canadian Longitudinal Study on Aging (CLSA), our objective is to explore how social, economic, and environmental factors contribute to nutrition risk in midlife and older adulthood. Our findings offer insights into targeted interventions and policy strategies to support aging in place and reduce health disparities.

2. Materials and Methods

2.1. Theoretical Framework

Our study employs social network theory, as articulated by Berkman and colleagues [35], as its theoretical framework. We chose this theory as our goal is to examine the psychosocial factors related to the development of high nutrition risk in Canadian adults. Social network theory provides a model for examining how social networks affect health [35]. Social networks are embedded in broader cultural and social contexts that influence how these social networks are structured [35]. These social networks, in turn, influence interpersonal and social behavior [35]. They do so through the following pathways: (1) social support, (2) social influence, (3) social engagement, and (4) access to resources [35]. Our study focuses on the micro level of social network theory as described by Berkman and collaborators [35], as the psychosocial mechanisms at this level of their theory may be determinants of nutrition risk and the development of nutrition risk.

2.2. Data Source

Our study is a secondary data analysis using data from the Canadian Longitudinal Study on Aging (CLSA). The CLSA is the most extensive and comprehensive prospective cohort study in Canada, focusing on the longitudinal examination of aging [36]. The participants in the CLSA were aged 45–85 years at the time of recruitment [37]. Participants are followed every three years for 20 years or until death [37]. Baseline data were collected between 2011 and 2015, and the first follow-up data (which we call follow-up) were collected during 2015–2018 [38]. Informed consent was obtained from all participants by the Canadian Longitudinal Study on Aging (CLSA) at the time of the original data collection. The present study involved a secondary analysis of de-identified data provided by the CLSA. No additional consent was required. Detailed information on CLSA’s informed consent procedures can be found at: https://www.clsa-elcv.ca/researchers (accessed on 6 June 2025).
The CLSA has two cohorts: tracking and comprehensive. The comprehensive cohort included 30,097 participants at baseline who were interviewed in person, underwent physical assessments, and provided urine and blood samples for analysis. The tracking cohort included 21,241 participants at baseline, followed by telephone interviews. Our study used data from the tracking cohort, as this cohort reflects Canadian provincial populations [36].
CLSA cohorts share the same core set of questionnaires. These questionnaires cover demographics, social and psychological measures, health and functional status, lifestyle, and behavior [38]. We used data from the baseline wave for psychosocial variables and data from the baseline and follow-up waves for nutrition risk. We mapped variables from the CLSA onto the psychosocial mechanisms of Berkman and colleagues’ social network theory (Table 1). We describe how the CLSA measured these variables below.

2.2.1. Psychosocial Mechanisms

Social support: The 19-item Medical Outcomes Study Social Support Survey (MOS) [39] was used to measure the participants’ social support. The MOS evaluates affection, emotional and informational support, tangible social support, and positive social interaction [39]. The MOS demonstrates excellent internal consistency, with Cronbach’s alpha values ranging from 0.91 to 0.97 for the overall scale and its subscales [39]. Additionally, it demonstrates robust and test–retest reliability, as indicated by an ICC of 0.78 after one year [39].
Self-rated social standing: The CLSA asked participants to “think of a ladder with 10 steps as representing where people stand in their communities. At the top of the ladder (or step 10) are the people who have the highest standing in their community. At the bottom (or step 1) are the people who have the lowest standing in their community. On which step would you place yourself on this ladder?” [40].
Social engagement: The CLSA asked participants how frequently they participated in sports or physical activities with other people and how frequently they participated in educational or cultural activities, in the past 12 months [41]. Responses were at least once a day, at least once a week, at least once a year, and never.
Total household income: The CLSA asked participants to estimate their total household income using the following categories: less than $20,000, $20,000 or more but less than $50,000, $50,000 or more but less than $100,000, $100,000 or more but less than $150,000, and $150,000 or more [41].
Access to health care: The CLSA asked participants if they had contact with a family physician or general practitioner about their physical or mental health in the past 12 months [40].
Housing: The CLSA asked participants, “When thinking of your home, how strongly would you agree or disagree with the following statement? I am satisfied with my current housing [40].” Participants could respond with strongly agree, agree, disagree, or strongly disagree.

2.2.2. Demographic Variables

Demographic variables collected by the CLSA included age, sex assigned at birth, educational attainment, and household composition (living alone or with others) [38].

2.2.3. Nutrition Risk

Nutrition risk was assessed in the CLSA using the abbreviated version of Seniors in the Community: Risk Evaluation for Eating and Nutrition II, now known as SCREEN-8 [42]. SCREEN-8 comprises eight questions related to typical daily eating habits and includes questions on weight change, meal skipping, swallowing, appetite, fruit and vegetable consumption, fluid intake, meal preparation, and eating with others [12]. SCREEN-8 scores range from 0 to 48, and scores lower than 38 indicate high nutrition risk (s.e. = 77%, SP = 64%, AUC = 78%) [12]. Therefore, we used this cutoff of SCREEN-8 less than 38, as recommended by the creator of SCREEN-8 [42], to determine which individuals developed high nutrition risk between baseline and follow-up. Compared to registered dietitians’ nutrition risk assessment, SCREEN-8 has good specificity and sensitivity (ρ = −0.62, p = 0.01) and its test–retest (ICC = 0.85) and inter-rater reliability (ICC = 0.85) of SCREEN-8 are also good [43].

2.3. Data Analysis

Our main analytical approach was multivariable binomial regression, as the outcome variable in our analyses was the presence or absence of high nutrition risk at follow-up. Consequently, our outcome variable had two levels [44]. We set the significance level at α ≤ 0.05, and reported confidence intervals and effect sizes.
First, we completed univariate analyses to determine the descriptive statistics for each variable in our analyses. Using the ten-year age groups reported by the CLSA, we then compared the different age groups on all the variables of interest, using one-way analysis of variance (ANOVA) for continuous variables, while for categorical variables we used chi-square (χ2) tests. We computed effect sizes, and used Cohen’s d for continuous variables and Cramer’s V for categorical variables [45].

Development of High Nutrition Risk at Follow-Up in Those Not at High Risk at Baseline

Next, we determined which participants were not at high nutrition risk at baseline. Using data from these participants, we performed multivariable binomial logistic regression analyses for the entire sample and the sample stratified by ten-year age groups. Our outcome variable was the presence or absence of high nutrition risk at follow-up, and the predictor variables were the psychosocial mechanisms. Subsequently, we conducted our regression analyses once more, incorporating demographic variables as potential covariates, given that previous studies have linked demographics with nutrition risk [20,21,22].
As the nutrition risk measure used in the CLSA has only been validated for those aged 50 years and older [20,42], we also conducted a sensitivity analysis for the 45–54 years old age group. We repeated our analyses for those aged 45–49 years old and those aged 50–54 years old and compared those to the results for the 45–54 years old age group.

3. Results

3.1. Sample Description

At baseline, there were 21,241 participants in the tracking cohort. At follow-up, data were available for 17,051 individuals, and we used this sample of 17,051 for all analyses. Between baseline and follow-up, three years later, 6.0% (n = 1266) of participants withdrew from the CLSA, 12.0% (n = 2546) were lost to follow-up, 1.8% (n = 377) had their follow-up data still in preparation at the time we received the data, and there were confirmed deaths for 5.5% (n = 1165) of participants.
At baseline, participants had a mean age of 59.46 (SD = 9.94), and the majority of participants were married or partnered (70.4%). At baseline, the mean SCREEN-8 score of the participants was 38.85 (SD = 6.30), and at follow-up, the mean SCREEN-8 score of the participants was 38.02 (SD = 6.53). At baseline, 34.3% of participants were identified as being at high nutrition risk, which increased to 40.0% at follow-up (Table 2).
When we compared the different age groups, we found statistically significant differences for all variables, except for sex assigned at birth. However, the effect size for most variables was trivial, except for household income, contact with a family physician or general practitioner, and marital status, where the effect size was small, and household composition, where the effect size was medium (Table 2).

3.1.1. Development of High Nutrition Risk at Follow-Up, by Ten-Year Age Group, in Those Who Were Not at High Risk at Baseline

Among those who were not at high nutrition risk at baseline, 27.4% (n = 2913) had developed high nutrition risk by follow-up. The 75 and older age group had the highest percentage of individuals developing high nutrition risk (29.2%), and the 65–74-year age group had the lowest percentage of individuals developing high nutrition risk at follow-up (24.4%).
When we examined the psychosocial factors associated with developing high nutrition risk between baseline and follow-up, we noted similarities and differences between the age groups. In all age groups, factors associated with the development of high nutrition risk between baseline and follow-up included social support, self-rated social standing, frequency of participation in sports or physical activities, frequency of participation in educational or cultural activities, and household income (Table 3). Among all age groups, for every one-point increase in social support and self-rating social standing, the odds of having high nutrition risk develop by follow-up decreased. There was also a trend that as the frequency of participation in sports or physical activities increased, the odds of developing high nutrition risk between baseline and follow-up decreased. Similarly, participating in educational or cultural activities at least once a year or more frequently was correlated with lower odds of developing high nutrition risk. For all age groups, as household income increased, there was a trend for lower odds of developing high nutrition risk.
Factors that differed for the oldest age group (≥75 years) included contact with a family physician or general practitioner and satisfaction with current housing (Table 3). For all but this oldest age group, having no contact with a family physician or general practitioner in the previous year was correlated with lower odds of developing high nutrition risk at follow-up. Additionally, for all but this oldest age group, there was a trend that as satisfaction with current housing decreased, the odds of developing high nutrition risk increased.
After controlling for demographic variables, there were a few differences in the factors correlated with the having high nutrition risk emerge between baseline and follow-up (Supplemental Table S1). For the youngest age group (aged 45–54 years), the frequency of participation in educational or cultural activities was no longer associated with the development of high nutrition risk, and household income only remained significantly associated with the development of high nutrition risk in this youngest age group. The demographic variables associated with the development of high nutrition risk were marital status, household composition, and educational attainment. For the 45–54- and 65–74-year age groups, at baseline, being single, as compared to being married or partnered, was associated with increased odds of having high nutrition risk appear between baseline and follow-up. For the 55–64-year age group, living with others was associated with lower odds of developing high nutrition risk compared to living alone. For the two youngest age groups (45–54 and 55–64 years), having a post-secondary degree or diploma, compared to having a less than secondary was associated with lower odds of having high nutrition risk appear between baseline and follow-up.
Sensitivity Analysis
We noted some differences after conducting our sensitivity analysis for those aged 45–49 and 50–54. In the analysis that examined the psychosocial factors only, none of the variables were statistically associated with developing high nutrition risk among those aged 45–49 (Supplemental Table S2). Similarly, when looking at both the psychosocial and demographic variables, none of these were associated with the transition from low nutrition risk at baseline to high nutrition risk at follow-up, among those aged 45–49 (Supplemental Table S3). Looking at those aged 50–54, there were some differences in the results compared to the 45–54 age group. Social support, physical activity, household income, and satisfaction with housing were all associated with the development of high nutrition risk among those aged 50–54 and in the age group 45–54. However, when examining the psychosocial variables for those aged 50–54, as opposed to those aged 45–54, self-rated social standing, participation in cultural activities, and contact with a family physician were not associated with the development of high nutrition risk. When demographic variables were added, lower household income was associated with higher odds of developing high nutrition risk in those aged 50–54, this association was not statistically significant in those aged 45–54.

4. Discussion

This study has examined the psychosocial factors associated with the development of high nutrition risk by ten-year age groups using a nationally representative Canadian sample. Our study contributes to the literature on nutrition risk in the community by expanding on previous research examining nutrition risk using the Canadian Longitudinal Study on Aging. While previous studies have examined nutrition risk using data from the CLSA [26,46,47], the authors are unaware of any studies using CLSA data that have examined the development of high nutrition risk in different age groups. We found that the psychosocial factors described in Berkman and colleagues’ social network theory [35] were associated with the development of high nutrition risk, providing further support for this social network theory. We found that many psychosocial factors related to the development of high nutrition risk were common across all age groups, while there were a few differences.
The psychosocial mechanisms associated with the development of high nutrition risk in all age groups and in both models were social support, self-rated social standing, and the frequency of participation in sports or physical activities. In the model that only included psychosocial mechanisms, the psychosocial mechanisms associated with the development of high-nutrition risk in all age groups were frequency of participation in educational or cultural activities and household income.
Similarly to previous work conducted using the CLSA [48], we found that social support was associated with the development of high nutrition risk, with higher levels of social support resulting in lower odds of developing high nutrition risk. Previous studies have identified an association between social support and nutrition risk in Canada [21,27] and the United States [32,49], where low levels of social support were associated with increased nutrition risk in adults aged 65 years and older. There are several potential mechanisms through which social support may influence nutrition risk. Social support systems may encourage healthy eating behaviors [32] and compliance with social norms around eating, such as regular meal times [25]. Social support systems can also provide assistance with food-related tasks, such as meal preparation, cooking, food provision, and grocery shopping, particularly when an individual needs assistance with these activities [33].
Similarly to our results showing that lower self-rated social standing is associated with developing high nutrition risk, previous studies using the CLSA comprehensive [50] and tracking cohorts [48] found a relationship between self-rated social standing and nutrition risk. Individuals who rate their social standing as higher may find it easier to access healthy foods and obtain adequate nutrition. In contrast, low social standing may indicate social disadvantage, which has been shown to influence the ability to afford healthy foods and the ability to spend time on food preparation [51].
Household income was also associated with developing high nutrition risk in our study, with higher household incomes lowering the odds of developing high nutrition risk. This connection between household income and nutrition risk has been found in a systematic review [52] and a scoping review [53]. Lower incomes are associated with food insecurity [54,55], which occurs when a household has “inadequate or insecure access to food due to financial constraints” [56] (p. 6). Individuals with lower incomes may not be able to afford sufficient food to meet their needs, leading to nutrition risk.
Similarly to previous research, we found that increased physical activity was associated with lower odds of having high nutrition risk develop in all age groups [57]. Individuals who are at high nutrition risk may not consume adequate amounts of food and nutrients to support physical activity. In contrast, individuals who are physically active are more likely to meet their nutrient needs, as energy expenditure during physical activity allows an individual to consume greater amounts of food, which, if nutrient-dense, can help them meet their nutrient requirements [58].
We also found that as the frequency of participation in educational or cultural activities increased the odds of having high nutrition risk develop decreased. To our knowledge, the association between educational or cultural activities and nutrition risk has not been previously explored in the literature. However, previous studies have examined the association between nutrition risk and participation in community activities. A Canadian study found that infrequent social participation (participating in community activities less than once per week) was associated with high nutrition risk [21]. Another study, conducted in Brazil, found that low social participation was associated with malnutrition risk [59]. Individuals who frequently participate in community activities may have higher levels of social support that enable them to engage in such activities. Additionally, individuals who participate in community activities more frequently may have more opportunities to eat with others. Consuming meals in the company of others has been demonstrated to enhance dietary intake and mitigate nutrition risk, whereas solitary eating is correlated with high nutrition risk [25,28].
Despite these similarities between age groups, there were some differences. Low levels of satisfaction with current housing were associated with the development of high nutrition risk in all but the oldest age group. This could be because older adults may have long been established in their homes and are comfortable with their housing situation. In contrast, younger individuals may be looking to buy their first home or to upsize or downsize their current homes. Similarly, lack of contact with a primary care provider was only associated with high nutrition risk in the three youngest age groups and not in the oldest age group. This observation likely indicates that a greater proportion of individuals within the oldest age cohort had interactions with their primary care providers in the past year than those in other age cohorts.
Our findings therefore reveal that psychosocial factors such as social participation, housing satisfaction, and social standing are significantly associated with nutrition risk. Cultural and educational participation emerged as protective factors, highlighting the importance of meaningful engagement in reducing nutrition risk. These findings align with Berkman’s social network theory [35], which posits that social integration influences health behavior and outcomes.
Age-specific differences were observed, with midlife adults showing stronger associations between educational attainment and nutrition risk, whereas older adults were more affected by housing satisfaction and social participation. These differences underscore the need for tailored interventions that address the unique challenges faced by each age group.
The policy implications of this study underscore the need for integrated, age-sensitive strategies to address nutrition risk and support aging in place. Primary care settings should incorporate routine nutrition risk screening, particularly using validated tools such as SCREEN-8, to identify individuals at risk early [60,61]. Programs such as Meals on Wheels can mitigate nutrition risk and social isolation among older adults by providing nutritious meals and regular social contact [62,63]. Community-based models in naturally occurring retirement communities or older adults’ recreation centers offer a promising approach by combining nutrition programming and education with social programming, fostering engagement, and reducing isolation [8,17]. One such model is Oasis Senior Supportive Living, which incorporates nutritional, physical, and social programming [8,64]. Oasis participants have reported improved health as a result of their participation in the program [64]. Community gardens [65,66] and intergenerational meal programs [67] can promote cultural and educational participation, which this study identified as protective factors against nutrition risk. For middle-aged adults, workplace wellness initiatives [68,69] and subsidized community kitchens [70] may address time constraints and financial barriers. Tailoring programs by age group, such as offering flexible participation options for working adults and mobility-friendly environments for older adults, can enhance their effectiveness. These interventions should be embedded within broader public health frameworks and supported by municipal and provincial policies that prioritize aging in place, nutrition, and social inclusion.

Limitations of the Study

As with all studies, this study has some limitations. Although the CLSA tracking cohort is generalizable to the Canadian population, several groups are not included. The CLSA cohorts do not include full-time Canadian Armed Forces members, those living in the Canadian territories and some remote areas, and those living on First Nations reserves and settlements [38]. Additionally, only individuals who speak English or French and those who can answer the questions themselves are included [38]. The tracking cohort also consists primarily of Canadians who indicated that their cultural/racial background is white (97.40%) [71]. The baseline CLSA data only includes individuals between the ages of 45 and 85 [38].
It is important to note that SCREEN-8 has only been validated for community-living adults aged 50 years and older [72]. Therefore, SCREEN-8 may not accurately measure nutrition risk in younger individuals, and we included adults aged 45–49, as well as those aged 50 and older. This posed a risk of misclassification bias [73], and when we separated those aged 50–54 from those aged 45–49, we noted several differences in the results, indicating that misclassification likely occurred. When only looking at those aged 50–54 (as opposed to those 45–54), self-rated social standing, participation in cultural activities, and contact with a family physician were no longer associated with developing high nutrition risk.
Furthermore, the manifestation of nutrition risk may vary among individuals aged 45–54. This demographic may experience changes in household composition, such as having children residing at home or children transitioning out of the household. These dynamics can influence dietary choices [74,75]. These individuals may also be working adults, affecting their food choices during the workday [74,75]. However, we included this younger age group in our study so that we could examine how high nutrition risk develops as individuals in this age group enter older adulthood using future waves of CLSA data.

5. Conclusions

Our research found similarities and differences among age groups in the psychosocial factors associated with the development of high nutrition risk in community-living Canadians. This suggests that interventions and policies aimed at reducing nutrition risk need to consider age. The development of nutrition risk is a critical barrier to aging in place, affecting both midlife and older adults. Factors commonly associated with the development of high nutrition risk across all age groups included social support, self-rated social standing, frequency of participation in sports or physical activities, frequency of participation in cultural or educational activities, and household income. Therefore, individuals who have low levels of social support, low self-rated social standing, low incomes, and those who participate infrequently in physical, educational, or cultural activities should be assessed for high nutrition risk if they are aged 45 years or older. As recommended by the Canadian Malnutrition Task Force [61], adults aged 65 years and older should also be screened annually for nutrition risk by their medical home [60]. Programs and policies that address psychosocial mechanisms (social support, social standing, income, and participation in community activities) need to be designed, implemented, and evaluated to reduce the prevalence of high nutrition risk and the progression from low to high nutrition risk. Interventions that combine nutrition programming with social activities, such as congregate dining [76,77], community kitchens [78] and other forms of social nutrition programming [8], may also help strengthen social ties while reducing nutrition risk. Nutrition risk reduction should be embedded in broader aging, housing, and health equity policies. Preventing the development of high nutrition risk can prevent the associated morbidity and mortality, thereby improving the health of adults at midlife and beyond. This, in turn, will support older adults to age in place in their communities safely and autonomously for as long as they desire.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jal6010004/s1, Table S1: Binomial logistic regression analyses exploring the development of high nutrition risk, controlling for demographics, Table S2: Binomial logistic regression analyses exploring the development of high nutrition risk for those aged 45–49 and 50–54, Table S3: Binomial logistic regression analyses exploring the development of high nutrition risk for those aged 45–49 and 50–54, controlling for demographics.

Author Contributions

Conceptualization, C.M.M. and C.D.; methodology, C.M.M.; formal analysis, C.M.M.; writing—original draft preparation, C.M.M.; writing—review and editing, C.D.; supervision, C.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board REH-722-18.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study by the Canadian Longitudinal Study on Aging. The present study is a secondary analysis of the first two waves of the CLSA.

Data Availability Statement

Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data.

Acknowledgments

This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation, as well as the following provinces, Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta, and British Columbia. This research has been conducted using the CLSA Baseline Tracking Dataset—Version 3.7, and Follow-up 1 Tracking Dataset—Version 2.2, under Application Number 2104008. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. The AB SCREENTM II, rebranded as SCREEN-8, assessment tool is owned by Heather Keller. Use of the AB SCREENTM II assessment tool was made under license from the University of Guelph.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AUCArea Under the Curve (for receiver operating characteristic curve)
CLSACanadian Longitudinal Study on Aging
CIConfidence interval
ICCIntraclass correlation
MOSMedical Outcomes Study Social Support Survey
OROdds ratio
SDStandard deviation
s.e.Standard error
SCREEN-8Seniors in the Community Risk Evaluation for Eating and Nutrition-8

References

  1. Statistics Canada. Older Adults and Population Aging Statistics. Available online: https://www.statcan.gc.ca/en/subjects-start/older_adults_and_population_aging (accessed on 27 March 2024).
  2. Fuster, V. Changing Demographics. J. Am. Coll. Cardiol. 2017, 69, 3002–3005. [Google Scholar] [CrossRef] [PubMed]
  3. United Nations Department of Economic and Social Affairs; Population Division. World Population Ageing 2020 Highlights; United Nations: New York, NY, USA, 2020; p. 47. [Google Scholar]
  4. Wheatley, M.C. Adapting Health Systems for an Aging World: Policy Innovations and Global Strategies. Prem. J. Public Health 2024, 1, 100010. [Google Scholar] [CrossRef]
  5. Khan, H.T.A.; Addo, K.M.; Findlay, H. Public Health Challenges and Responses to the Growing Ageing Populations. Public Health Chall. 2024, 3, e213. [Google Scholar] [CrossRef] [PubMed]
  6. National Institute on Aging. Enabling the Future Provision of Long-Term Care in Canada; National Institute on Ageing White Paper: Toronto, ON, Canada, 2019. [Google Scholar]
  7. The Forum of Federal/Provincial/Territorial Ministers Responsible for Seniors. Thinking About Your Future? Plan Now to Age in Place; The Forum of Federal/Provincial/Territorial (F/P/T) Ministers Responsible for Seniors: Ottawa, ON, Canada, 2015; p. 20. [Google Scholar]
  8. Keller, H.H. Promoting Food Intake in Older Adults Living in the Community: A Review. Appl. Physiol. Nutr. Metab. 2007, 32, 991–1000. [Google Scholar] [CrossRef]
  9. Payette, H.; Coulombe, C.; Boutier, V.; Gray-Donald, K. Nutrition Risk Factors for Institutionalization in a Free-Living Functionally Dependent Elderly Population. J. Clin. Epidemiol. 2000, 53, 579–587. [Google Scholar] [CrossRef]
  10. Elsner, R.J.F. Changes in Eating Behavior during the Aging Process. Eat. Behav. 2002, 3, 15–43. [Google Scholar] [CrossRef]
  11. de Boer, A.; Ter Horst, G.J.; Lorist, M.M. Physiological and Psychosocial Age-Related Changes Associated with Reduced Food Intake in Older Persons. Ageing Res. Rev. 2013, 12, 316–328. [Google Scholar] [CrossRef]
  12. Keller, H.H. Using and Scoring SCREEN. Available online: https://olderadultnutritionscreening.com/faq/ (accessed on 11 October 2022).
  13. Lundin, H.; Sääf, M.; Strender, L.-E.; Mollasaraie, H.A.; Salminen, H. Mini Nutritional Assessment and 10-Year Mortality in Free-Living Elderly Women: A Prospective Cohort Study with 10-Year Follow-Up. Eur. J. Clin. Nutr. 2012, 66, 1050–1053. [Google Scholar] [CrossRef]
  14. Broeska, V.E.; Lengyel, C.O.; Tate, R.B. Nutritional Risk and 5-Year Mortality of Older Community-Dwelling Canadian Men: The Manitoba Follow-Up Study. J. Nutr. Gerontol. Geriatr. 2013, 32, 317–329. [Google Scholar] [CrossRef]
  15. Keller, H.H.; Østbye, T.; Goy, R.; Ostbye, T.; Goy, R.; Østbye, T.; Goy, R. Nutritional Risk Predicts Quality of Life in Elderly Community-Living Canadians. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2004, 59, M68–M74. [Google Scholar] [CrossRef]
  16. Dent, E.; Visvanathan, R.; Piantadosi, C.; Chapman, I. Nutritional Screening Tools as Predictors of Mortality, Functional Decline, and Move to Higher Level Care in Older People: A Systematic Review. J. Nutr. Gerontol. Geriatr. 2012, 31, 97–145. [Google Scholar] [CrossRef] [PubMed]
  17. Macasaet, M.; Dhaliwal, R.; Chan, C.B. Addressing Nutrition Risk in Older Adults in Community Settings. Med. Res. Arch. 2024, 12. [Google Scholar] [CrossRef]
  18. Krznarić, Ž.; Vranešić Bender, D.; Blaž Kovač, M.; Cuerda, C.; Van Ginkel-Res, A.; Hiesmayr, M.; Marinho, A.; Mendive, J.; Monteiro, I.; Pirlich, M.; et al. Clinical Nutrition in Primary Care: ESPEN Position Paper. Clin. Nutr. 2024, 43, 1678–1683. [Google Scholar] [CrossRef] [PubMed]
  19. Phillips, M.B.; Foley, A.L.; Barnard, R.; Isenring, E.A.; Miller, M.D. Nutritional Screening in Community-Dwelling Older Adults: A Systematic Literature Review. Asia Pac. J. Clin. Nutr. 2010, 19, 440–449. [Google Scholar]
  20. Morrison, J.M.; Laur, C.; Keller, H.H. SCREEN III: Working towards a Condensed Screening Tool to Detect Nutrition Risk in Community-Dwelling Older Adults Using CLSA Data. Eur. J. Clin. Nutr. 2019, 73, 1260–1269. [Google Scholar] [CrossRef]
  21. Ramage-Morin, P.L.; Garriguet, D. Nutritional Risk among Older Canadians. Health Rep. 2013, 24, 3–13. [Google Scholar]
  22. Borkent, J.W.; Keller, H.H.; Wham, C.A.; Wijers, F.; de van der Schueren, M.A.E. Cross-Country Differences and Similarities in Undernutrition Prevalence and Risk as Measured by SCREEN II in Community-Dwelling Older Adults. Healthcare 2020, 8, 151. [Google Scholar] [CrossRef]
  23. Kaiser, M.J.; Bauer, J.M.; Rämsch, C.; Uter, W.; Guigoz, Y.; Cederholm, T.; Thomas, D.R.; Anthony, P.S.; Charlton, K.E.; Maggio, M.; et al. Frequency of Malnutrition in Older Adults: A Multinational Perspective Using the Mini Nutritional Assessment. J. Am. Geriatr. Soc. 2010, 58, 1734–1738. [Google Scholar] [CrossRef]
  24. Wolters, M. Prevalence of Malnutrition Using Harmonized Definitions in Older Adults from Different Settings—A MaNuEL study. Clin. Nutr. 2019, 38, 2389–2398. [Google Scholar] [CrossRef]
  25. Vesnaver, E.; Keller, H.H. Social Influences and Eating Behavior in Later Life: A Review. J. Nutr. Gerontol. Geriatr. 2011, 30, 2–23. [Google Scholar] [CrossRef]
  26. Ingham, N.; Labonté, K.; Dube, L.; Paquet, C.; Nielsen, D.E. A More Supportive Social Environment May Protect Against Nutritional Risk: A Cross-Sectional Analysis Using Data From the Canadian Longitudinal Study on Aging. J. Nutr. 2023, 153, 1793–1802. [Google Scholar] [CrossRef] [PubMed]
  27. Johnson, C.S.J. Psychosocial Correlates of Nutritional Risk in Older Adults. Can. J. Diet. Pract. Res. 2005, 66, 95–97. [Google Scholar] [CrossRef] [PubMed]
  28. Bloom, I.; Lawrence, W.; Barker, M.; Baird, J.; Dennison, E.; Sayer, A.A.; Cooper, C.; Robinson, S. What Influences Diet Quality in Older People? A Qualitative Study among Community-Dwelling Older Adults from the Hertfordshire Cohort Study, UK. Public Health Nutr. 2017, 20, 2685–2693. [Google Scholar] [CrossRef] [PubMed]
  29. Wham, C.A.; Teh, R.; Moyes, S.; Dyall, L.; Kepa, M.; Hayman, K.; Kerse, N. Health and Social Factors Associated with Nutrition Risk: Results from Life and Living in Advanced Age: A Cohort Study in New Zealand (LILACS NZ). J. Nutr. Health Aging 2015, 19, 637–645. [Google Scholar] [CrossRef]
  30. Wham, C.A.; McLean, C.; Teh, R.; Moyes, S.; Peri, K.; Kerse, N. The BRIGHT Trial: What Are the Factors Associated with Nutrition Risk? J. Nutr. Health Aging 2014, 18, 692–697. [Google Scholar] [CrossRef]
  31. McElnay, C.; Marshall, B.; O’Sullivan, J.; Jones, L.; Ashworth, T.; Hicks, K. Nutritional Risk amongst Community-Living Maori and Non-Maori Older People in Hawke’s Bay. J. Prim. Health Care 2012, 4, 299–305. [Google Scholar] [CrossRef]
  32. Locher, J.L.; Sharkey, J.R. An Ecological Perspective on Older Adult Eating Behavior. In Handbook of Clinical Nutrition and Aging; Bales, C.W., Ritchie, C.S., Eds.; Humana Press: Totowa, NJ, USA, 2009; pp. 3–17. ISBN 978-1-60327-384-8. [Google Scholar]
  33. Keller, H.H. Reliance on Others for Food-Related Activities of Daily Living. J. Nutr. Elder. 2005, 25, 43–59. [Google Scholar] [CrossRef]
  34. Hamirudin, A.H.; Charlton, K.; Walton, K. Outcomes Related to Nutrition Screening in Community Living Older Adults: A Systematic Literature Review. Arch. Gerontol. Geriatr. 2016, 62, 9–25. [Google Scholar] [CrossRef]
  35. Berkman, L.F.; Glass, T.; Brissette, I.; Seeman, T.E. From Social Integration to Health: Durkheim in the New Millennium. Soc. Sci. Med. 2000, 51, 843–857. [Google Scholar] [CrossRef]
  36. Raina, P.; Wolfson, C.; Kirkland, S.A.; Griffith, L.E.; Oremus, M.; Patterson, C.; Tuokko, H.; Penning, M.; Balion, C.M.; Hogan, D.; et al. The Canadian Longitudinal Study on Aging (CLSA). Can. J. Aging/Rev. Can. Vieil. 2009, 28, 221–229. [Google Scholar] [CrossRef]
  37. Kirkland, S.; Griffith, L.E.; Menec, V.; Wister, A.; Payette, H.; Wolfson, C.; Raina, P.S. Mining a Unique Canadian Resource: The Canadian Longitudinal Study on Aging. Can. J. Aging/Rev. Can. Vieil. 2015, 34, 366–377. [Google Scholar] [CrossRef] [PubMed]
  38. Raina, P.; Wolfson, C.; Kirkland, S.; Griffith, L.E.; Balion, C.; Ct Cossette, B.; Dionne, I.; Hofer, S.; Hogan, D.; Van Den Heuvel, E.R.; et al. Cohort Profile: The Canadian Longitudinal Study on Aging (CLSA). Int. Epidemiol. Assoc. Int. J. Epidemiol. 2019, 48, 1752–1753. [Google Scholar] [CrossRef] [PubMed]
  39. Sherbourne, C.D.; Stewart, A.L. The MOS Social Support Survey. Soc. Sci. Med. 1991, 32, 705–714. [Google Scholar] [CrossRef] [PubMed]
  40. Canadian Longitudinal Study on Aging. CLSA Maintaining Contact Questionnaire (Tracking and Comprehensive); Canadian Longitudinal Study on Aging: Hamilton, ON, Canada, 2015; p. 84. [Google Scholar]
  41. Canadian Longitudinal Study on Aging. CLSA 60-Min. Questionnaire (Tracking Main Wave); Canadian Longitudinal Study on Aging: Hamilton, ON, Canada, 2018; p. 189. [Google Scholar]
  42. Keller, H.H. SCREEN Tools. Available online: https://olderadultnutritionscreening.com/ (accessed on 29 March 2022).
  43. Keller, H.H.; Goy, R.; Kane, S.-L. Validity and Reliability of SCREEN II (Seniors in the Community: Risk Evaluation for Eating and Nutrition, Version II). Eur. J. Clin. Nutr. 2005, 59, 1149–1157. [Google Scholar] [CrossRef]
  44. Osborne, J.W. Best Practices in Logistic Regression; SAGE Publications, Ltd.: London, UK, 2015. [Google Scholar]
  45. Durlak, J.A. How to Select, Calculate, and Interpret Effect Sizes. J. Pediatr. Psychol. 2009, 34, 917–928. [Google Scholar] [CrossRef]
  46. Keller, H.H.; Trinca, V. Determinants of a Decline in a Nutrition Risk Measure Differ by Baseline High Nutrition Risk Status: Targeting Nutrition Risk Screening for Frailty Prevention in the Canadian Longitudinal Study on Aging (CLSA). Can. J. Public Health 2023, 114, 593–612. [Google Scholar] [CrossRef]
  47. Trinca, V.; Keller, H. Nutrition Risk Is Associated with 3-Year Strength and Performance Indicators among Older Adults in the Canadian Longitudinal Study on Aging. J. Nutr. Health Aging 2023, 27, 868–877. [Google Scholar] [CrossRef]
  48. Mills, C.M.; Keller, H.H.; DePaul, V.G.; Donnelly, C. Factors Associated with the Development of High Nutrition Risk: Data from the Canadian Longitudinal Study on Aging. Can. J. Aging/Rev. Can. Vieil. 2024, 43, 153–166. [Google Scholar] [CrossRef]
  49. Locher, J.L.; Ritchie, C.S.; Roth, D.L.; Baker, P.S.; Bodner, E.V.; Allman, R.M. Social Isolation, Support, and Capital and Nutritional Risk in an Older Sample: Ethnic and Gender Differences. Soc. Sci. Med. 2005, 60, 747–761. [Google Scholar] [CrossRef]
  50. Chopp-Hurley, J.N.; Wiebenga, E.G.; Keller, H.H.; Maly, M.R. Diet and Nutrition Risk Affect Mobility and General Health in Osteoarthritis: Data from the Canadian Longitudinal Study on Aging. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2020, 75, 2147–2155. [Google Scholar] [CrossRef]
  51. Mattes, R.D.; Rowe, S.B.; Ohlhorst, S.D.; Brown, A.W.; Hoffman, D.J.; Liska, D.J.; Feskens, E.J.M.; Dhillon, J.; Tucker, K.L.; Epstein, L.H.; et al. Valuing the Diversity of Research Methods to Advance Nutrition Science. Adv. Nutr. 2022, 13, 1324–1393. [Google Scholar] [CrossRef] [PubMed]
  52. Thompson Martin, C.; Kayser-Jones, J.; Stotts, N.; Porter, C.; Froelicher, E.S. Nutritional Risk and Low Weight in Community-Living Older Adults: A Review of the Literature (1995-2005). J. Gerontol. Ser. A Biol. Sci. Med. Sci. 2006, 61, 927–934. [Google Scholar] [CrossRef] [PubMed]
  53. Nazri, N.S.; Vanoh, D.; Leng, S.K. Malnutrition, Low Diet Quality and Its Risk Factors among Older Adults with Low Socio-Economic Status: A Scoping Review. Nutr. Res. Rev. 2021, 34, 107–116. [Google Scholar] [CrossRef] [PubMed]
  54. Leroux, J.; Morrison, K.; Rosenberg, M. Prevalence and Predictors of Food Insecurity among Older People in Canada. Int. J. Environ. Res. Public Health 2018, 15, 2511. [Google Scholar] [CrossRef]
  55. Sharkey, J.R. Nutrition Risk Screening: The Interrelationship of Food Insecurity, Food Intake, and Unintentional Weight Change among Homebound Elders. J. Nutr. Elder. 2004, 24, 19–34. [Google Scholar] [CrossRef]
  56. Li, T.; Fafard St-Germain, A.; Tarasuk, V. Household Food Insecurity in Canada, 2022; Research to identify policy options to reduce food insecurity; PROOF: Toronto, ON, Canada, 2023. [Google Scholar]
  57. Wham, C.A.; Teh, R.O.Y.; Robinson, M.; Kerse, N.M. What Is Associated with Nutrition Risk in Very Old Age? J. Nutr. Health Aging 2011, 15, 247–251. [Google Scholar] [CrossRef]
  58. Hammond, G.; O’Connor, C.; Piché, L.A.; Whitney, E.N.; Rolfes, S.R. Understanding Nutrition, 3rd ed.; Cengage Canada: Toronto, ON, Canada, 2023; ISBN 1-77841-038-3. [Google Scholar]
  59. Damião, R.; Meneguci, J.; da Silva Santos, Á.; Matijasevich, A.; Rossi Menezes, P. Nutritional Risk and Quality of Life in Community-Dwelling Elderly: A Cross-Sectional Study. J. Nutr. Health Aging 2018, 22, 111–116. [Google Scholar] [CrossRef]
  60. Keller, H.H.; Donnelly, R.; Laur, C.; Goharian, L.; Nasser, R. Consensus-Based Nutrition Care Pathways for Hospital-to-Community Transitions and Older Adults in Primary and Community Care. J. Parenter. Enter. Nutr. 2021, 46, 141–152. [Google Scholar] [CrossRef]
  61. Canadian Malnutrition Task Force. Nutrition Care Pathways. Available online: https://nutritioncareincanada.ca/resource-library/primary-community-care/nutrition-care-pathways (accessed on 3 February 2025).
  62. Keller, H.H.; Hedley, M.R.; Wong, S.S.L.; Vanderkooy, P.; Tindale, J.; Norris, J. Community Organized Food and Nutrition Education: Participation, Attitudes and Nutritional Risk in Seniors. J. Nutr. Health Aging 2006, 10, 15–20. [Google Scholar]
  63. Gadbois, E.; Brazier, J.; Turner, J.; Hawes, C.; Florence, L.; Belazis, L. “It Made Me Feel Like a Person Again”: Benefits of Meals on Wheels-Based Social Connection Programs. J. Appl. Gerontol. 2024, 43, 1924–1938. [Google Scholar] [CrossRef]
  64. Mills, C.M.; Parniak, S.; Depaul, V.G.; Donnelly, C. Oasis Senior Supportive Living: Description of a Novel Aging-in-Place Model in Ontario. Can. J. Diet. Pract. Res. 2023, 84, 119–122. [Google Scholar] [CrossRef] [PubMed]
  65. Mills, C.M.; Parniak, S.; Hand, C.; McGrath, C.; Laliberte Rudman, D.; Chislett, C.; Giberson, M.; White, L.; DePaul, V.; Donnelly, C. The Impact of a Naturally Occurring Retirement Community Supportive Services Program on Older Adult Participants’ Social Networks: Semistructured Interview Study. JMIR Aging 2022, 5, e37617. [Google Scholar] [CrossRef] [PubMed]
  66. Scott, T.L.; Masser, B.M.; Pachana, N.A. Positive Aging Benefits of Home and Community Gardening Activities: Older Adults Report Enhanced Self-Esteem, Productive Endeavours, Social Engagement and Exercise. SAGE Open Med. 2020, 8, 2050312120901732. [Google Scholar] [CrossRef] [PubMed]
  67. Guo, J.; Yanai, S.; Xu, G. Community Gardens and Psychological Well-Being among Older People in Elderly Housing with Care Services: The Role of the Social Environment. J. Environ. Psychol. 2024, 94, 102232. [Google Scholar] [CrossRef]
  68. Bruce, J.S.; Lien, T.N.; George, E.; Puri, V.; Ramirez, M.; Merrell, S.B. Examination of an Intergenerational Summer Meal Program for Children and Older Adults. J. Community Health 2022, 47, 902–913. [Google Scholar] [CrossRef]
  69. Downes, L.S.; Buchholz, S.W.; Fogg, L.F. Impact of a Nutrition Education Program on Health Behaviors in a University Workplace Wellness Program. J. Am. Assoc. Nurse Pract. 2021, 33, 1198. [Google Scholar] [CrossRef]
  70. Peñalvo, J.L.; Sagastume, D.; Mertens, E.; Uzhova, I.; Smith, J.; Wu, J.H.Y.; Bishop, E.; Onopa, J.; Shi, P.; Micha, R.; et al. Effectiveness of Workplace Wellness Programmes for Dietary Habits, Overweight, and Cardiometabolic Health: A Systematic Review and Meta-Analysis. Lancet Public Health 2021, 6, e648–e660. [Google Scholar] [CrossRef]
  71. Iacovou, M.; Pattieson, D.C.; Truby, H.; Palermo, C. Social Health and Nutrition Impacts of Community Kitchens: A Systematic Review. Public Health Nutr. 2013, 16, 535–543. [Google Scholar] [CrossRef]
  72. Canadian Longitudinal Study on Aging. Cultural/Racial Background. Available online: https://datapreview.clsa-elcv.ca/mica/variable/trm%3ASDC_DCGT_TRM%3ACollected#/ (accessed on 25 April 2023).
  73. Keller, H.H. Nutri-eSCREEN®: Descriptive Analysis of a Self-Management Site for Older Adults (50+ Years). BMC Nutr. 2016, 2, 1. [Google Scholar] [CrossRef]
  74. Pham, A.; Cummings, M.; Lindeman, C.; Drummond, N.; Williamson, T. Recognizing Misclassification Bias in Research and Medical Practice. Fam. Pract. 2019, 36, 804–807. [Google Scholar] [CrossRef]
  75. Booth, S.L.; Sallis, J.F.; Ritenbaugh, C.; Hill, J.O.; Birch, L.L.; Frank, L.D.; Glanz, K.; Himmelgreen, D.A.; Mudd, M.; Popkin, B.M.; et al. Environmental and Societal Factors Affect Food Choice and Physical Activity: Rationale, Influences, and Leverage Points. Nutr. Rev. 2009, 59, S21–S36. [Google Scholar] [CrossRef]
  76. Chen, P.-J.; Antonelli, M. Conceptual Models of Food Choice: Influential Factors Related to Foods, Individual Differences, and Society. Foods 2020, 9, 1898. [Google Scholar] [CrossRef]
  77. Juckett, L.A.; Lee, K.; Bunger, A.C.; Brostow, D.P. Implementing Nutrition Education Programs in Congregate Dining Service Settings: A Scoping Review. Gerontologist 2022, 62, e82–e96. [Google Scholar] [CrossRef]
  78. Keller, H.H. Meal Programs Improve Nutritional Risk: A Longitudinal Analysis of Community-Living Seniors. J. Am. Diet. Assoc. 2006, 106, 1042–1048. [Google Scholar] [CrossRef]
Table 1. Mapping CLSA 1 measures onto psychosocial mechanisms from Social Network Theory [35].
Table 1. Mapping CLSA 1 measures onto psychosocial mechanisms from Social Network Theory [35].
Psychosocial MechanismsCLSA 1 Measures
Social supportMedical Outcomes Study Social Support Survey
Social influence
  • Social comparison processes
  • Self-rated social standing
Social engagement
  • Physical/cognitive exercise
  • Frequency of participation in sports or physical activities with others
  • Frequency of participation in educational or cultural activities
Access to resources and material goods
  • Economic opportunity
  • Access to health care
  • Housing
  • Total household income
  • Contact with a general practitioner or family physician
  • Satisfied with current housing
1 Canadian Longitudinal Study on Aging.
Table 2. Characteristics of the CLSA tracking cohort, overall and stratified by ten-year age groups.
Table 2. Characteristics of the CLSA tracking cohort, overall and stratified by ten-year age groups.
CharacteristicPopulation Estimate 1
(n = 17051)		45–54
(38.9%, n = 6633)		55–64
(32.4%, n = 5525)		65–74
(18.8%, n = 3206)		75+
(9.9%, n = 1688)		Age Group Difference Effect Size 2
(p-Value)
SCREEN-8 score at baseline, mean (SD)38.85 (6.30)38.67 (6.34)38.87 (6.51)39.18 (6.06)38.86 (5.86)0.002 (<0.001)
SCREEN-8 score at follow-up, mean (SD)38.02 (6.53)38.09 (6.69)37.97 (6.56)38.19 (6.35)37.56 (6.10)0.001 (0.027)
At high nutrition risk at baseline, % (n)34.3 (5705)37.0 (1728)34.7 (1854)32.0 (1182)32.4 (941)0.042 (<0.001)
At high nutrition risk at follow-up, % (n)40.0 (6618)41.0 (2787)40.2 (2157)37.8 (1382)40.7 (1144)0.025 (0.018)
Developed high nutrition risk between baseline and follow-up, % (n)27.4 (2913)25.7 (749)24.8 (853)24.4 (597)29.2 (540)0.038 (0.002)
Social support, mean (SD)83.75 (17.25)84.33 (16.67)83.46 (17.34)83.77 (17.81)82.26 (18.15)0.001 (<0.001)
Tangible Social Support, mean (SD)82.05 (20.84)81.50 (20.30)81.83 (21.18)83.67 (21.18)81.30 (21.74)0.002 (<0.001)
Emotional and Informational Social Support, mean (SD) 82.10 (19.23)82.90 (18.41)82.16 (19.33)82.41 (18.92)80.24 (20.63)0.002 (<0.001)
Positive Social Interaction, mean (SD)83.45 (19.57)83.38 (19.31)83.65 (19.42)84.44 (19.00)81.92 (20.85)0.002 (<0.001)
Affection, mean (SD)86.85 (19.77)87.99 (18.78)86.37 (20.30)86.62 (20.02)86.16 (19.93)0.001 (<0.001)
Self-rated social standing, mean (SD)5.81 (2.07)5.92 (2.03)5.80 (2.05)5.75 (2.09)5.70 (2.16)0.001 (<0.001)
Frequency of participation in sports or physical activities with others, % (n)
Never
At least once a year
At least once a month
At least once a week
At least once a day		29.7 (5063)
6.4 (1091)
13.9 (2369)
42.1 (7170)
7.8 (1335)		23.0 (1105)
7.9 (378)
17.3 (828)
44.8 (2149)
7.0 (338)		27.8 (1525)
6.7 (367)
15.2 (831)
42.2 (2313)
8.0 (440)		31.4 (1185)
5.2 (198)
12.0 (452)
42.3 (1599)
9.1 (343)		41.9 (1248)
5.0 (148)
8.7 (258)
37.3 (1109)
7.2 (214)		0.090 (<0.001)
Frequency of participation in educational or cultural activities
Never
At least once a year
At least once a month
At least once a week
At least once a day		19.7 (3353)
32.7 (5571)
37.3 (6347)
9.6 (1633)
0.8 (130)		15.5 (742)
35.3 (1696)
39.7 (1907)
8.1 (390)
1.3 (64)		18.7 (1026)
33.3 (1823)
38.5 (2108)
9.0 (491)
0.6 (33)		20.7 (780)
31.0 (1168)
36.4 (1371)
11.5 (432)
0.5 (33)		27.0 (805)
29.6 (884)
32.2 (961)
10.7 (320)
0.4 (13) 		0.069 (<0.001)
Household income, %
Less than $20,000
$20,000–49,999
$50,000–99,999
$100,000–149,999
$150,000 or more		5.5 (887)
27.5 (4407)
37.3 (5983)
17.3 (2770)
12.3 (1972)		3.9 (176)
12.6 (582)
32.9 (1524)
27.8 (1285)
22.9 (1061)		4.7 (244)
23.7 (1233)
41.1 (2135)
18.2 (943)
12.3 (637)		6.7 (233)
38.6 (1351)
40.7 (1424)
9.1 (320)
4.9 (173)		8.7 (234)
46.0 (1241)
33.4 (900)
8.2 (822)
3.7 (101)		0.221 * (<0.001)
Contact with a general practitioner or family physician, % (n)
Yes		90.1 (15112)84.9 (3978)89.9 (4840)93.5 (3497)94.5 (2797)0.123 * (<0.001)
Satisfied with current housing, % (n)
Strongly agree
Agree
Disagree
Strongly disagree		66.7 (11149)
30.0 (5009)
2.7 (449)
0.7 (113)		64.4 (3013)
30.9 (1445)
3.6 (168)
1.1 (53)		67.4 (3617)
29.1 (1558)
2.8 (150)
0.7 (38)		69.3 (2585)
27.8 (1038)
2.4 (90)
0.4 (15)		65.6 (1934)
32.8 (968)
1.4 (41)
0.2 (7)		0.041 (<0.001)
Age, mean (SD)59.46 (9.94)49.77 (2.80)59.67 (2.83)68.88 (2.86)78.90 (2.99)
Sex, % (n)
Male 		48.3 (8240)48.0 (2305)47.7 (2614)49.1 (1854)49.1 (1467)0.012 (0.451)
Marital status, % (n)
Married or partnered
Single
Widowed		70.4 (12007)
19.7 (3356)
9.9 (1682)		77.2 (3708)
21.5 (1032)
1.3 (61)		73.6 (4036)
21.7 (1191)
4.6 (253)		68.9 (2604)
19.3 (728)
11.8 (445)		55.5 (1659)
13.6 (405)
30.9 (923)		0.247 * (<0.001)
Education, % (n)
Less than secondary
Secondary
Some post-secondary
Post-secondary degree/diploma		7.6 (1289)
12.9 (2196)
7.6 (1290)
71.9 (12216)		3.6 (174)
11.2 (537)
6.7 (323)
78.4 (3760)		5.3 (289)
14.0 (763)
8.1 (441)
72.7 (3967)		8.9 (334)
13.7 (518)
7.4 (277)
70.0 (2639)		16.5 (491)
12.7 (378)
8.4 (249)
62.3 (1850)		0.104 (<0.001)
Household composition, % (n)
Lives alone		21.8 (3724)10.9 (523)19.0 (1042)25.8 (974)39.7 (1185)0.236 ** (<0.001)
1 Population estimates were calculated using trimmed inflation weights as recommended by the CLSA, 2 Effect size for differences between age groups: eta-squared (η2) for continuous variables and Cramér’s V for categorical variables, * small effect size, ** medium effect size.
Table 3. Binomial logistic regression analyses exploring the development of high nutrition risk in CLSA tracking participants not at high nutrition risk at baseline, using variables measured at baseline, by ten-year age group.
Table 3. Binomial logistic regression analyses exploring the development of high nutrition risk in CLSA tracking participants not at high nutrition risk at baseline, using variables measured at baseline, by ten-year age group.
CharacteristicOR 195% CI 1p-ValueOR 195% CI 1p-ValueOR 195% CI 1p-ValueOR 195% CI1p-ValueOR 195% CI 1p-Value
Age group45–54
(38.9%, n = 6633)		55–64
(32.4%, n = 5525)		65–74
(18.8%, n = 3206)		75+
(9.9%, n = 1688)		 Overall
Social support0.9850.981, 0.990<0.0010.9850.981, 0.989<0.0010.9870.982, 0.991<0.0010.9850.980, 0.990<0.0010.9900.987, 0.994<0.001
Self-rated social standing0.9390.908, 0.971<0.0010.9480.918, 0.9790.0010.9530.917, 0.9910.0150.9350.894, 0.9770.0030.9730.949, 0.9980.036
Frequency of participation in sports or physical activities
Never—— —— —— —— ——
At least once a year0.8810.678, 1.1450.3450.8310.640, 1.0790.1660.8460.593, 1.2020.3541.0990.712, 1.6870.6660.8560.689, 1.0590.155
At least once a month0.8130.661, 0.9990.0490.8250.677, 1.0040.0550.8430.652, 1.0890.1930.9810.701, 1.3660.9080.9460.808, 1.1060.485
At least once a week0.6110.514, 0.726<0.0010.6060.518, 0.708<0.0010.7970.662, 0.9600.0170.8410.680, 1.0400.1090.7630.675, 0.862<0.001
At least once a day0.4860.360, 0.651<0.0010.4870.372, 0.633<0.0010.5420.395, 0.737<0.0010.6570.447, 0.9530.0290.5410.438, 0.664<0.001
Frequency of participation in educational or cultural activities
Never—— —— —— —— ——
At least once a year0.8460.688, 1.0400.1120.6900.574, 0.829<0.0010.9820.791, 1.2210.8720.7790.609, 0.9980.0480.9120.790, 1.0540.213
At least once a month0.8090.656, 1.0000.0490.6150.511, 0.741<0.0010.7380.592, 0.9200.0070.8250.640, 1.0630.1360.7550.652, 0.876<0.001
At least once a week0.8500.634, 1.1370.2750.5420.414, 0.709<0.0010.7540.563, 1.0080.0580.6800.480, 0.9590.0290.7250.593, 0.8860.002
At least once a day0.6920.363, 1.2720.2470.8930.376, 2.0820.7931.6740.599, 4.6150.3150.2010.011, 1.1260.1340.8960.501, 1.5360.700
Total household income
Less than $20,000—— —— —— —— ——
$20,000 or more, but less than $50,0000.5490.350, 0.8440.0070.6810.480, 0.9610.0300.6580.469, 0.9190.0150.7710.548, 1.0840.1350.8230.631, 1.0790.154
$50,000 or more, but less than $100,0000.4030.262, 0.608<0.0010.5170.366, 0.725<0.0010.4760.338, 0.669<0.0010.5520.386, 0.7880.0010.6660.512, 0.8720.003
$100,000 or more, but less than $150,0000.3150.203, 0.479<0.0010.5860.408, 0.8380.0040.4290.282, 0.648<0.0010.7420.473, 1.1620.1930.6680.505, 0.8880.005
$150,000 or more0.3110.199, 0.477<0.0010.4800.328, 0.700<0.0010.4660.286, 0.7530.0020.3150.164, 0.582<0.0010.5420.404, 0.731<0.001
Contact with a family physician or general practitioner
Yes—— —— —— —— ——
No0.7700.640, 0.9230.0050.7460.603, 0.9190.0060.7010.505, 0.9610.0300.8100.522, 1.2380.3360.7690.651, 0.9060.002
Satisfied with current housing
Strongly agree—— —— —— —— ——
Agree1.0930.949, 1.2590.2181.2131.056, 1.3930.0061.1160.939, 1.3250.2131.1560.950, 1.4060.1470.9780.877, 1.0900.690
Disagree1.6491.143, 2.3870.0082.3611.578, 3.569<0.0011.5820.960, 2.6200.0722.1230.874, 5.3700.1001.8411.333, 2.528<0.001
Strongly disagree2.0021.050, 3.9280.0381.5600.707, 3.5170.27315.792.904, 293.60.0090.5970.072, 3.5370.5880.8450.305, 2.0170.722
1 OR = Odds Ratio, CI = Confidence Interval, p-values in bold are significant at p < 0.05.
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Mills, C.M.; Donnelly, C. Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging. J. Ageing Longev. 2026, 6, 4. https://doi.org/10.3390/jal6010004

AMA Style

Mills CM, Donnelly C. Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging. Journal of Ageing and Longevity. 2026; 6(1):4. https://doi.org/10.3390/jal6010004

Chicago/Turabian Style

Mills, Christine Marie, and Catherine Donnelly. 2026. "Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging" Journal of Ageing and Longevity 6, no. 1: 4. https://doi.org/10.3390/jal6010004

APA Style

Mills, C. M., & Donnelly, C. (2026). Examining Psychosocial Factors Influencing Nutrition Risk in Middle-Aged and Older Adults: Findings from the Canadian Longitudinal Study on Aging. Journal of Ageing and Longevity, 6(1), 4. https://doi.org/10.3390/jal6010004

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