Next Article in Journal
Conditions for Nutritional Care of Elderly Individuals with Dementia and Their Caregivers: An Exploratory Study
Next Article in Special Issue
Estimation of Intake of Fat, Saturated Fatty Acids, and Trans Fatty Acids from Sweet and Salty Snacks Among Children and Adolescents
Previous Article in Journal
Effect of Combined Vitamin C and Thiamine Therapy on Myocardial and Inflammatory Markers in Cardiac Surgery: A Randomized Controlled Clinical Trial
Previous Article in Special Issue
Relationship Between Metabolic Age Determined by Bioimpedance and Insulin Resistance Risk Scales in Spanish Workers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 17
Issue 6
10.3390/nu17061005
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Gender, Adverse Changes in Social Engagement and Risk of Unhealthy Eating: A Prospective Cohort Study of the Canadian Longitudinal Study on Aging (2011–2021)

by
Sanaz Mehranfar
1,
Gilciane Ceolin
2,
Rana Madani Civi
2,
Heather Keller
3,4,
Rachel A. Murphy
5,6,
Tamara R. Cohen
1,7 and
Annalijn I. Conklin
1,2,8,9,*
1
Food, Nutrition and Health, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
3
Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada
4
Schlegel-UW Research Institute for Aging, Waterloo, ON N2J 0E2, Canada
5
School of Population and Public Health, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
6
Cancer Control Research, BC Cancer Research Centre, Vancouver, BC V5Z 1L3, Canada
7
Healthy Starts, BC Children’s Hospital Research Institute, BC Children’s Hospital, Vancouver, BC V5Z 4H4, Canada
8
Centre for Advancing Health Outcomes, Providence Health Care Research Institute, St. Paul’s Hospital, Vancouver, BC V6Z 1Y6, Canada
9
Edwin S.H. Leong Centre for Healthy Aging, Faculty of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(6), 1005; https://doi.org/10.3390/nu17061005
Submission received: 5 February 2025 / Revised: 6 March 2025 / Accepted: 8 March 2025 / Published: 13 March 2025
(This article belongs to the Special Issue Public Health, Nutritional Behavior and Nutritional Status—2nd Edition)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: Social isolation is linked to survival and health. However, dietary effects of social activities, and gender differences, over time are unknown. Methods: A prospective study of adults (45+y) reporting daily fruit or vegetable (F/V) intake (at wave 1) from the Canadian Longitudinal Study on Aging (CLSA). Multivariable mixed logistic regression assessed changes in social isolation or breadth of social participation (wave 1 to 2) in relation to adverse changes in F/V (non-daily intake) at wave 3 in women and men. Results: Women who remained socially isolated between waves 1 and 2 had 85% higher odds of non-daily vegetable intake (OR 1.85 [95% CI: 1.32, 2.59]) and over twofold higher odds of non-daily fruit intake (2.23 [1.58, 3.14]), compared to reference (not isolated at waves 1 and 2). Higher odds of non-daily F/V intake were also observed for women who changed from isolated at wave 1 to not isolated at wave 2. Women and men who had less diverse social participation at waves 1 and 2 had 28–64% higher odds of non-daily F/V intake, compared to their counterparts with diverse social participation at both waves. Higher odds of non-daily fruit were also seen for women who had diverse social participation at wave 1 but reduced their diversity at wave 2 (1.35 [1.12, 1.62]). Conclusions: Results showed persistent social isolation impacted changes in F/V among women only, while limited breadth of social participation affected F/V intake in both genders. Further longitudinal research on the complexities of social engagement and eating behavior is warranted.

1. Introduction

By 2030, one in six people worldwide will be aged 60 or older [1]. The demographic shift in Canada will surge in 2031 when all baby boomers will have turned 65, with older adults comprising about 25% of the population by 2036 [2]. Aging in late adulthood frequently accompanies significant changes in the social environment, which includes a high degree of social isolation [3]. Almost one-third of Canadian older adults are at risk of social isolation [4]. Thus, remaining socially connected and socially active is viewed as the foremost emerging issue for older adults according to the International Federation on Ageing [5].
The extent to which a person engages in social activities where they interact with others in the community can affect health and well-being, particularly in late adulthood [6,7]. It is known that social integration is linked to higher odds of survival [8] and overall healthy aging [9]. Specifically, higher social participation is linked to better self-perceived health and reduced feelings of loneliness and dissatisfaction [10]. Conversely, social isolation is associated with a higher risk of mortality [11,12]; however, the evidence is limited by reliance on social isolation scores (e.g., the Shankar index) [13] that combine lone-living, infrequent social contact, no social participation, and not married [13,14,15,16,17,18]. Although social isolation and loneliness are often studied together or used interchangeably, social isolation is an objective structural indicator (i.e., absence of others) whereas feelings of loneliness are a subjective functional indicator (i.e., an emotional state) [11,19]. Scope exists to disentangle social isolation in healthy aging research given that structural social ties show stronger effects on health outcomes [11] and risk factors [20,21,22,23].
Social relationships are theorized to impact health through multiple pathways [24], including health-related behaviors such as diet [25]. Indeed, the social environment is known to influence food intake and diet [26]. Specifically, social isolation has been linked to lower dietary quality [7,27], reduced intake of fruits and vegetables (FV) [14,28,29], lower dietary micronutrient intake [30], and lower dietary variety [31]. By contrast, social participation is associated with lower nutritional risk [32], better diet quality [33], and higher FV intake [34]. Much of the current literature, however, is limited to cross-sectional studies that lack temporality between the social factors and diet and/or lack assessment of alterations in social integration over time, such as becoming socially isolated or reducing social participation. To date, no study has examined the changes in social participation in relation to changes in healthful eating indicators among aging adults [35]. Moreover, nutritional epidemiology research rarely disaggregates data on women and men despite the fact that gender dynamics significantly influence both eating habits [36] and social participation [10,37]. Thus, great scope exists to investigate whether changes in social isolation and in the breadth of social participation reduce or increase dietary fruit or vegetable intake and, especially, to understand whether women and men differ in vulnerability to the strength of impact.
This study leverages three waves of data from a Canadian aging cohort to prospectively examine whether adverse changes in social isolation (lack of activities) or breadth of social participation (variety of activities) are associated with the development of unhealthful eating habits among older women and men. We tested the hypothesis that (1) adverse changes (e.g., becoming socially isolated or reducing the breadth of social participation) will be associated with adverse change in healthful eating (i.e., a decline in daily fruit or vegetable (F/V) intake) and that (2) associations will differ by gender. Our approach addresses critical knowledge gaps in the aging literature by considering both the heterogeneous dynamics of social ties over time and the temporal separation of social tie transitions and dietary behavior changes.

2. Materials and Methods

2.1. Study Population and Eligible Sample

We used three waves of data from 30,097 predominantly White, non-institutionalized, middle-age and older adults (45–85 years) in the Canadian Longitudinal Study on Aging (CLSA) Comprehensive Cohort—a stratified random sample of community-dwelling individuals [38]. This cohort had self-reported data and anthropometric measurements collected from English- or French-speaking individuals residing within a 25–50 km of 11 specified data collection sites during three waves. This cohort excluded residents of the three territories, federal First Nations reserves and other First Nations settlements in the provinces, remote regions, full-time members of the Canadian Forces, and individuals living in institutions or with cognitive impairment at the time of recruitment [39,40]. To address the under-representation of individuals with lower education levels and socioeconomic status in population-based studies, special efforts were undertaken to oversample specific areas identified through census data [41]. The detailed CLSA study design is available elsewhere [39,40,41]. In order to assess decline in healthful eating (non-daily F/V), our eligible sample included CLSA participants who reported consuming fruits (n= 21,178, 74%) or vegetables (n = 18,333, 64%) at least one time per day at baseline (wave 1: 2011–2015) and, also, who had information on social participation and other covariables (respectively, n = 18,413 or n = 15,999) (details of sample size in Supplemental Figure S1). We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies [42].

2.2. Changes in Fruit or Vegetable Intake

Our primary outcome was the adverse change from healthful eating (daily F/V intake) at wave 1 (2011–2015) to less healthful eating (non-daily F/V intake) at wave 3 (2018–2021); that is, the development (new-onset) of less healthful eating over 6-year follow-up. Thus, we considered less healthful eating as fruit intake less than once per day (non-daily fruit) and vegetable intake less than once per day (non-daily vegetable) using dietary data collected by CLSA’s Short Diet Questionnaire (SDQ). The SDQ is a validated 36-item non-quantitative food frequency questionnaire (FFQ) [43] that was developed and used in the CLSA to assess usual consumption frequencies (last 12 months) of key nutrients and foods of importance for health promotion and chronic disease prevention in community-dwelling middle-age and older adults [43]. Five relevant questions (fruit, green vegetables, potatoes, carrots, and other vegetables) were available for analysis. We defined healthful eating (at least daily intake of fruits or vegetables) [14] based on frequency rather than serving size in accordance with Canada’s Food Guide [44] that does away with portion sizes. CLSA responses for consumption of food items were daily, weekly, monthly, or yearly, followed by another question of “how many times” for that frequency response (e.g., twice a day, three times a week, once a month). CLSA converted all SDQ responses into times per day that we dichotomized for fruits (0 = less than daily, 1 = daily), green vegetables (0 = less than daily, 1 = daily), potatoes (0 = less than daily, 1 = daily), carrots (0 = less than daily, 1 = daily), and other vegetables (0 = less than daily, 1 = daily). The vegetable outcome variable was based on any of the four possible items being coded as daily intake (details of coding method in Supplemental Table S1). We excluded extreme values of fruit or vegetable intake that may result from measurement error or non-representative unique observations. To maintain consistency in our approach to outlier exclusion, we followed the National Cancer Institute Dietary Screener Questionnaire in the US NHANES 2009–2010 method [45] to exclude outliers in daily intake (>8 times/day for fruit; >5 times/day for vegetables including carrots, green vegetables, and others; and >3 times/day for potatoes) before constructing binary F/V variables at wave 1 (to restrict the sample) and wave 3 (to assess outcome).

2.3. Changes in Social Isolation and Breadth of Social Participation

We assessed changes in social isolation and in breadth of social participation based on CLSA data on the number of different social activities. CLSA asked participants a group of questions about the frequency of social activities in the last year across eight settings: ‘in the past 12 months, how often did you participate in [family, religious, sports, educational/cultural, social clubs, charity, neighborhood, and other recreational activities]?’. For each setting, we considered regular participation to be at least once a month or more (coded as 1) versus less than monthly (coded as 0), similar to previous research [20,21,46], and then counted the number of activities at waves 1 and 2 to create a total activity score with good internal consistency (Cronbach’s α of 0.66). We considered the lower extreme of zero or 1 activity per month (scores combined due to few observations of zero) to assess social isolation at both waves; we, also, classified the upper extreme of 5 or more activities per month to assess breadth of social participation at both waves [20,21,46], which represented the highest tertile of the total activity score. The social isolation transition variable was classified as: (1) remained not isolated (no change in 2 or more activities at both waves, reference); (2) remained isolated (no change in 0 or 1 activity at both waves); (3) became socially isolated (transition to 0 or 1 activity); and (4) became not isolated (transition to 2 or more activities). The breadth of social participation transition variable was classified as: (1) remained diverse (no change in ≥5 activities at both waves, reference); (2) remained less diverse (no change in <5 activities at both waves); (3) became less diverse (transition from ≥5 activities to <5 activities); and (4) became diverse (transition from <5 activities to ≥5 activities). The social participation transition variable was weakly correlated with the social isolation transition variable (Cramer’s V = 0.20).

2.4. Covariables

Study duration was calculated in months between wave 1 and wave 3. Supplemental Figure S2 shows the Directed Acyclic Graph (DAG) we created with DAGitty software (version 3.0; Nijmegen, GE, The Netherlands) [47] to include theoretical, plausible, and known confounders [48,49,50,51,52,53,54,55,56]: (1) biological factors (age, body mass index (BMI) (proxy of energy intake), one or more chronic conditions, including depression); (2) socioeconomic status indicators (education (post-secondary graduation [university degree], some post-secondary education, secondary school graduation [high school diploma], below secondary school graduation [less than high school diploma]), household income (≥CAD 150,000, ≥CAD 100,000 to CAD 149,999, ≥CAD 50,000 to CAD 99,999, ≥CAD 20,000 to CAD 49,999, <CAD 20,000, do not know/refuse), wealth (home-owner, renter), and rural/urban location); (3) behavioral factors (at least 7 h/day of sleep); and (4) provincial factors (gross domestic product (GDP), public spending, food insecurity, and consumer price index for F/V). Provincial covariables were obtained from national statistics based on the average recruitment date of participants in each province.

2.5. Sex and Gender Considerations

At wave 1, participants answered ‘are you male or female?’, with responses representing biological sex and/or gender identity. Additional CLSA data on “gender identity” (male, female, transgender, transman, transwoman, genderqueer, and other) were collected at wave 2; both CLSA variables very strongly correlated (Cramer’s V = 0.998), with very few non-cisgender observations excluded (n = 40). Thus, we interpret results for women and men as reflecting cisgender identity, gender roles, gender relations, and institutionalized gender [57].

2.6. Statistical Analysis

Descriptive analysis used means (SD) and frequencies (%) to describe sample characteristics across the categories of each transition variable and applied CLSA survey inflation weights to produce estimates that represent the entire population of Canadians aged 45 to 85 residing in the vicinity of the data collection sites [41]. Main analysis examined the prospective associations of social participation or social isolation changes with developing non-daily fruit or non-daily vegetable intake in aging women and men. Multivariable mixed-effect logistic regressions (new-onset non-daily intake) used a random coefficient model (individuals nested within geographical areas) to account for the natural nesting of CLSA data and included an interaction term (transition variable x gender) to generate ORs specific to women and men. We ran duplicate regressions using reverse coding of the interaction term (women = 0 and men = 1; women = 1 and men = 0) to provide estimates for each reference group from the same model. Inferential statistics did not include analytic weights because CLSA does not provide sampling weights at both the primary sampling unit (Level 1) and the geographical level (Level 2), which are necessary for a fully-weighted multilevel model [58]. Model fit was tested using likelihood ratio tests and Akaike and Bayesian information criteria. Post-estimation of regression coefficients then calculated the average predicted probability of non-daily intake of fruits or vegetables associated with each category of transition (STATA ‘margins’ command), which were plotted for ease of interpretation (‘marginsplot’). Final results are reported as odds ratios (OR), or average predicted probabilities, with 95% confidence intervals (95% CI). Finally, sample sizes varied by outcome (vegetable n = 15,999; fruit n = 18,413). Analyses were carried out using Stata/SE 18.
Sensitivity analyses of main results were further conditioned on other potential confounders or independent predictors based on previous literature, including oral health [59], smoking [60], alcohol consumption [61], and, for women, reproductive variables (pregnancy, menopausal status, and hormone replacement therapy usage) [62]. For vegetable intake, we additionally excluded potatoes from the list of vegetable items. Final models were also adjusted for other wave 1 social relationships (e.g., living arrangement, marital status, and social network) to determine the independent effects of each transition variable. To assess the robustness of our findings regarding the impact of the COVID-19 lockdown on dietary behavior, we further excluded participants interviewed from 1 April 2020 onwards similar to another Canadian nutrition study [63].

3. Results

The average duration between CLSA waves 1 and 3 was 70.21 months (SD 5.81). For the smallest analytic sample (vegetable outcome), the mean age was 62 years (SD 9.9) and 59.0% were women. The majority of the sample remained not socially isolated between the first two waves of the CLSA cohort (91.64% of women and 89.53% of men). Nevertheless, 5.6% of women and 7.2% of men experienced adverse transitions, either remaining socially isolated or becoming socially isolated over time. Only about one-third of the sample’s social participation remained diverse (33.5% of women and 28.7% of men), and a much larger proportion experienced adverse changes (54.8% of women and 60.3% of men remained less diverse or became less diverse). Around 1 in 5 aging adults in our sample developed less healthful eating over the 6-year study duration, with 23% reporting intakes of vegetables less than once per day and 17.0% reporting intakes of fruits less than once per day.
Table 1 and Table 2 show an uneven distribution of sociodemographic characteristics across transitions in, respectively, social isolation and social participation over 3 years for women and men who reported daily intake of vegetables at wave 1. Variation in the development of non-daily vegetable intake across all the transitions in social isolation was more notable than across the transitions in breadth of social participation, with a more pronounced variation observed among men (Table 2) than among women (Table 1). Sample characteristics for the sample of fruit intake are given in Supplemental Table S2.

3.1. Social Isolation and Social Participation Transitions Associated with Reduced Vegetable Intake by Gender

Final models indicated that specific social isolation transitions were prospectively associated with subsequent non-daily vegetable intake among women only (Table 3). Specifically, women remaining socially isolated had 85% higher odds of new-onset non-daily vegetable intake (OR 1.85 [95% CI: 1.32, 2.59]), compared to women who remained not isolated. Women who became not socially isolated also had higher odds of new-onset non-daily vegetable intake (OR 1.38 [95% CI: 1.03, 1.84]), compared to the referent. No association was observed among men. By contrast, both women and men maintaining less diverse social participation (<5 activities/month at both waves) had higher odds of new-onset non-daily vegetable intake (respectively, 1.30 [95% CI: 1.15, 1.47]) and (1.29 [95% CI: 1.13, 1.46]), compared to counterparts remaining diverse in social participation (Table 4).
Sensitivity analysis of additional confounders did not alter the significant findings for women remaining socially isolated (Supplemental Table S3) or results for both women and men remaining less diverse in their social participation (Supplemental Table S4). Notably, results for women becoming not isolated were attenuated by adjustment for oral health and other social ties. To check robustness of findings to the influence of COVID-19 on eating habits, we excluded participants interviewed from 1 April 2020 onwards and found associations remained largely unchanged or even strengthened (in men for social isolation transitions) (Supplemental Tables S3 and S4).
Figure 1 illustrates greater variation and more pronounced gender differences in the average predicted probability of reduced vegetable intake associated with distinct social isolation transitions (panel A) than social participation transitions (panel B) over time. The highest predicted probability of reduced vegetable intake occurred among women remaining socially isolated (28%), but among men becoming not socially isolated (37%). The lowest predicted probability of reduced vegetable intake for both women (18%) and men (31%) was observed among those remaining not socially isolated. The predicted probability of reduced vegetable intake for social isolation and social participation transitions was independent of other structured social ties (Supplemental Figure S3).

3.2. Social Isolation and Social Participation Transitions Associated with Reduced Fruit Intake by Gender

Final models indicated that specific social isolation transitions were prospectively associated with subsequent non-daily fruit intake among women only (Table 5). Specifically, women remaining socially isolated had over twofold higher odds of new-onset non-daily fruit intake (OR 2.23 [95% CI: 1.58, 3.14]), compared to women who remained not isolated. Women who became not socially isolated also had higher odds of new-onset non-daily fruit intake (OR 1.77 [95% CI: 1.33, 2.37]), compared to the referent. No association was observed among men. By contrast, women and men maintaining less diverse social participation had higher odds of new-onset non-daily fruit intake (respectively, 1.64 [95% CI: 1.43, 1.88]) and (1.28 [95% CI: 1.12, 1.46]) and, also, women whose participation became less diverse (1.35 [95% CI: 1.12, 1.62]), compared to counterparts remaining diverse in social participation (Table 6).
Sensitivity analysis of additional confounders did not alter the main findings for women or men (Supplemental Tables S5 and S6), with two exceptions. Adjusting for oral health and alcohol consumption strengthened results for men remaining or becoming socially isolated (Supplemental Table S5). The exclusion of participants interviewed during the COVID-19 lockdown did not affect the results for any of the exposures (Supplemental Tables S5 and S6).
Figure 2 illustrates variations in the average predicted probability of reduced fruit intake associated with a change in social isolation that was more pronounced for women than for men (panel A). The highest predicted probability of reduced fruit intake occurred among women remaining socially isolated (25%) and the lowest probability was among women remaining not socially isolated (13%). Variation in the average predicted probability of reduced fruit intake associated with social participation transitions was more pronounced among women than men (panel B). The predicted probability of reduced fruit intake for social isolation and social participation transitions was independent of other structured social ties (Supplemental Figure S4).

4. Discussion

This population-based prospective study revealed that specific transitions in both social isolation and breadth of social participation were significantly associated with developing less healthful eating among aging Canadians, and that associations were gendered. Although we did not find that becoming socially isolated reduced healthful eating, we did find that persistent social isolation and reduced breadth of social participation were both associated with a decline in healthful eating among women. We also found that maintaining less diverse social participation was also associated with a decline in healthful eating among women. However, our findings for men did not support our hypothesis that becoming socially isolated or reducing the breadth of social participation would be associated with a decline in the daily F/V intake. Nevertheless, we did show that maintaining less diverse social participation was associated with non-daily F/V intake among men. Our finding that becoming not socially isolated was associated with reduced healthful eating among aging women was unexpected and indicated that the transition out of social isolation may not be protective of diet quality for this subpopulation. Finally, an important finding from this research is that the dietary effect of changes in both social isolation and social participation was robust to other factors and, also, independent of other social relationships.

4.1. Relevance to Previous Research

In general, social relationships (both present and absent) are linked to longevity outcomes [8,11] and are also known to influence diet quality, particularly FV consumption, among older adults [31,64,65]. Recent research illustrates that aging women more than men, particularly of the oldest age and socioeconomic disadvantage, were more likely to become socially isolated or less diverse in social participation [46]. Yet, there is an evidence gap on whether alterations in an older person’s social environment, as a disruption in our homeostatic need for social connection, are true risk factors of healthful eating among aging adults. Our recent systematic review of longitudinal research on changes in social relationships and FV intake identified a large knowledge gap as current research only focuses on marital transitions and changes in FV intake [35]. This study is the first, to the best of our knowledge, to examine distinct alterations in social isolation and in the diversity of social participation as determinants of two indicators of healthful eating among middle-aged and older women and men in Canada.
Existing cross-sectional research indicates that social isolation is associated with poor health behavior profiles, such as low FV intake [29], which contributes to poor diet quality [25]. Across Europe, adults reporting high levels of social isolation were more likely to be physically inactive and to consume fewer fruits or vegetables on a daily basis compared to those reporting low/moderate levels of social isolation [14]. However, data were not disaggregated for women, transitions in isolation were not assessed, and social isolation was analyzed as a composite index, making results difficult to compare with this study. Indeed, the majority of health literature on social relationships continues to use composite scores for social isolation [66], which not only obscures intervention targets for tailored social prescribing but also masks potential gender differences for redressing health inequity. In a cross-sectional study of British older adults, lower levels of friend contact were associated with reduced variety of fruits and of vegetables in a graded trend for both genders (though more pronounced among men), and weekly family contact was also associated with women’s healthful eating indicators [31]. This study showed only women who remained socially isolated over three years were more likely to reduce their healthful eating in the subsequent three years (independent of marital status and living arrangement), which appears consistent with previous research.
The gender-specific dietary effect of remaining socially isolated among aging adults may be explained by differences in access to resources due to gender roles, relations, and institutional structures [67]. Women, who live longer than men, are more likely to live alone and to be more constrained by gender inequity that disproportionately shapes the more limited social (and economic) resources and opportunities available (and allowed) for women to support healthful behaviors and achieve good health [68]. Historically, women have had limited access to financial and social resources that constrain their ability to fully participate in society. For example, until the mid-1960s, Canadian women’s financial security was closely tied to their relationships with men, reinforcing economic dependency and unequal access to resources [69]. This dependency, coupled with women’s historically limited access to and control over family or household resources compared to men, not only reflects but also reinforces their unequal opportunities for full social and economic participation. Following divorce, women often experience more chronic financial strain, such as a decrease in household income and greater risk of poverty, that has negative consequences for women’s health [70], and especially for FV intake given FV are more costly food items per kcal. Other explanations for the observed decline in healthful eating among women who remained socially isolated include less social control over eating behaviors [71], absence of gendered social norms of women eating healthy [72], or lack of different types of social support for healthy behaviors [24].
A surprising and seemingly contradictory finding of this study was the adverse dietary consequences for women who transition out of social isolation. To understand this unexpected finding, we investigated the eight component activities for this transition category (Supplemental Figures S5 and S6) and observed that women who became not socially isolated added monthly sport and other recreational activities (e.g., hobbies, gardening, poker, bridge, and cards). We, therefore, postulate that transitioning into regular recreational activities, which commonly involve sedentary games, may be accompanied by unhealthful snacking during social gatherings that replaced daily F/V intake. It is known that card-related games in a gambling setting are correlated with alcohol consumption, which might increase snack intake [73]. Similarly, while data on the type of sport are unavailable, golf is a popular activity among older adult White Canadians [74], which is often accompanied by alcohol consumption [75] that is known to pattern with poorer dietary habits [61]. Thus, this study demonstrates that transitioning out of social isolation may not offer a protective benefit for healthful eating among aging women and may depend on the setting of women’s social engagement. Findings need to be replicated in other cohorts and more nutrition research on this transition out of social isolation is needed.
An added novelty of this study is the assessment of the breadth of social participation. Other literature on social participation indicates that engaging in frequent social activities is linked to better psychosocial and health-related outcomes [76]; specifically, frequent involvement in diverse social activities can positively impact health, reduce loneliness, and improve life satisfaction [10]. Some nutrition studies indicate that higher levels of social participation are correlated with lower nutritional risk in older adults [32,77] and higher FV consumption, especially among lone-living older adults [34]. This study demonstrated that a persistent lack of breadth in social participation (maintaining less diverse social activities each month) was associated with reduced healthful eating, using both FV indicators among women and among men in Canada. Importantly for women, loss of breadth in social participation (becoming less diverse) was also associated with loss of daily fruit intake over time. In our post hoc exploration, we observed that women in this transition category disengaged from regular neighborhood, community, or professional association activities, volunteer work, charity work, educational or cultural activities, and sports (Supplemental Figure S7). Broadly, our results support the notion that different types of relationships may fulfill distinct health-related needs, contributing to specific benefits [78]. In parallel to dietary guidelines advocating for FV variety to ensure nutrient balance, a corresponding recommendation of regular engagement with a variety of relationships and social roles may benefit different social needs and, in turn, diet [78].
Overall, this study revealed a clear gendered pattern of stronger associations between specific transitions in social isolation or breadth of social participation and healthful eating indicators among aging women. Women generally have larger networks [79,80] that extend beyond marriage and tend to rely on their friendship ties over time more than men [81]. Women’s social networks are more diverse and serve a wider range of functions compared to those of men [82]; thus, the broader social environment may be more impactful for women’s health-promoting behaviors. Our study showed that both social isolation and breadth of social participation transitions appear to matter for shaping women’s FV intake over time. It is suggested that participation in social relationships promotes health behaviors by providing opportunities for sociability, meaningful roles, and shared norms [24]. So, diverse social relationships reflect different gender roles, which can influence eating behaviors by assigning women caregiving responsibilities, such as food preparation and meal planning, or social responsibilities such as community organizing. As a result, women are more likely to engage in shared food practices and social eating routines. When women experience reduced social participation or become isolated, they lose access to their multiple gender roles that, then, disrupt their dietary habits. In contrast, men’s eating behaviors are less closely tied to caregiving roles and social expectations around food [83], making them less affected by changes in social connectedness, regardless of their cooking skills. Additionally, aging brings various challenges, such as financial stress or other health-related stressors, which disproportionately affect women who typically respond to stress through more pro-social strategies [84]. Hence, the lack of or reduced variety of social contacts could more negatively impact older women’s diets through compromised coping mechanisms. Our gender-specific findings on the relationship between social tie changes and changes in FV intake warrant further investigation to identify the influence of gender roles and societal norms that may shape how social transitions impact eating habits in older women.
Canada’s vision for healthy aging emphasizes social connectedness and healthy eating [85], and our findings offer actionable insights for designing gender-responsive interventions that promote both. Policies can prioritize increasing social engagement among older adults through community-based programs that encourage participation in diverse social activities. These programs should address barriers to engagement—such as mobility issues, transportation, and social stigma—while enhancing facilitators like accessible venues and inclusive programming. Incorporating nutritional support within these initiatives could further amplify their impact. For example, providing tailored nutritional information, free or subsidized meals, community gardens, and cooking workshops can directly support healthier eating behaviors. Indeed, fostering social participation is not only linked to improved dietary habits but also presents a valuable opportunity to deliver targeted nutritional interventions. Given that diet is a modifiable risk factor for chronic conditions, integrating social and nutritional support in community settings is essential for improving the well-being of aging populations. Furthermore, recognizing gender differences in how social activities influence eating behaviors is crucial for designing effective, equitable interventions. These efforts can help ensure that aging Canadians remain socially connected while benefiting from improved nutrition and overall health, ultimately supporting healthier aging trajectories.

4.2. Methodological Considerations

Participant recall errors in the frequency of fruit and vegetable intake were likely to result in non-differential misclassification bias in the outcome that would bring our estimates towards the null and underestimate associations. Self-reported frequency of social activity in the past year may also be prone to recall bias; however, such misclassification would be the same at all CLSA waves and, therefore, would not likely affect the exposure assessment of social isolation or social participation transitions over time. To reduce recall bias in dietary and social data, future studies could employ more objective collection methods, possibly using monitoring technologies; quantitative approaches to dietary assessment methods would also allow for calculating total energy intake. Results were limited by residual confounding due to imperfect measurement of covariables and unmeasured confounding due to missing dietary data on total energy intake, serving sizes, and food preparation skills. Given our dietary data were collected for one-third of the sample during the COVID-19 pandemic, the pandemic may have introduced reporting bias and altered our estimate of changes in healthy eating; the pandemic may also have altered some of the social activities following our exposure assessment in waves 1 and 2. Nevertheless, our sensitivity analysis that excluded participants interviewed from 1 April 2020 onwards (during the COVID-19 lockdown) demonstrated our results are robust. Lastly, as the CLSA cohort is predominantly White (96%), cisgender (99%), and heteronormative (98%) population, our findings cannot be generalized to more diverse populations or other settings.
Nonetheless, this study offers numerous strengths. These include two indicators of healthful eating, assessment of distinct transitions in both social isolation and breadth of social participation, adjustment for multiple known confounders, consideration of provincial sociopolitical differences, advanced statistical techniques to account for nested data, and application of a gender perspective. This study makes a novel contribution to the social relationship and health literature by examining alterations in the social environment and assessing a single measure of social isolation based on the absence of monthly social activity (independent of marital status, living arrangement, and social network size). Additionally, our analytic strategy of a target trial-emulated framework enabled a more rigorous estimation to support causal inference by ensuring temporality between changes in both exposures and outcomes among participants who were free of the outcome at wave 1 [86].

5. Conclusions

This prospective population-based study revealed that distinct changes in both social isolation and breadth of social participation were associated with indicators of healthful eating among aging adults in Canada, and that these associations were gendered. We found that persistent lack of regular social activity and both the persistent lack and loss of diversity in social activities had an impact on both indicators of healthful eating among women in particular. Only the persistent lack of diverse social activities appeared to shape healthful eating among men. The complexity of changes in the social environment on changes in diet among aging adults requires further investigation, especially through a gender lens.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu17061005/s1, Figure S1. Flow diagram of the process of sample selection from the Canadian Longitudinal Study on Aging (2011–2021). Figure S2. Directed Acyclic Graph (DAG) for social isolation (A) and social participation (diversity of social participation) (B) and fruit and vegetable intake. Figure S3. Average predicted probability of non-daily vegetable intake associated with social isolation transitions and social participation transitions among women and men in the Canadian Longitudinal Study on Aging (2011–2021). Figure S4. Average predicted probability of non-daily fruit intake associated with social isolation transitions and social participation transitions among women and men in the Canadian Longitudinal Study on Aging (2011–2021). Figure S5. Frequency of social activities across social isolation transitions using analytic sample size of vegetable among women. Figure S6. Frequency of social activities across social isolation transitions using analytic sample size of fruit among women. Figure S7. Frequency of social activities across social participation transitions using analytic sample size of fruit among women. Table S1. Coding process of mock examples of dietary data from the CLSA Short Diet Questionnaire. Table S2. Sample characteristics across social isolation transitions and social participation transitions among aging women and men in the eligible sample consuming daily fruit intake in the Canadian Longitudinal Study on Aging (2011–2021). Table S3. Sensitivity analysis of the associations between social isolation transitions and non-daily vegetable intake among aging women and men in the Canadian Longitudinal Study on Aging (2011–2021). Table S4. Sensitivity analysis of the associations between social participation transitions and non-daily vegetable intake among aging women and men in the Canadian Longitudinal Study on Aging (2011–2021). Table S5. Sensitivity analysis of the associations between social isolation transitions and non-daily fruit intake among aging women and men in the Canadian Longitudinal Study on Aging (2011–2021). Table S6. Sensitivity analysis of the associations between social participation transitions and non-daily fruit intake among aging women and men in the Canadian Longitudinal Study on Aging (2011–2021).

Author Contributions

A.I.C. conceptualized the study and submitted the data request and proposal to CLSA scientific committee. A.I.C., R.A.M., H.K. and S.M. designed the analysis. S.M. performed the data analysis. S.M., G.C. and R.M.C. contributed to data cleaning and variable preparation. S.M. and A.I.C. drafted the original manuscript. All authors contributed to interpretation of the data, and all authors revised the article critically for important intellectual content. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by a grant from the Social Sciences and Humanities Research Council (430-2022-00440). A.I.C. receives salary and research support from a Michael Smith Health Research BC Scholar Award (SCH-2020-0581).

Institutional Review Board Statement

This secondary data analysis study was approved by the University of British Columbia] Behavioural Research Ethics Board (H22-02057-A001) on 22 July 2022.

Informed Consent Statement

Informed consent was obtained in writing from all CLSA participants recruited for the CLSA cohort study.

Data Availability Statement

Data are available through a formal data access request to the Canadian Longitudinal Study on Aging.

Acknowledgments

This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the 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 was conducted using the CLSA Baseline Comprehensive version 7.0; Follow-up 1 Comprehensive version 3.2; and Follow-up 2 Comprehensive version 2.0, under application number 2206013. The CLSA is led by Parminder Raina, Christina Wolfson, and Susan Kirkland. The development, testing, and validation of the Short Diet Questionnaire (SDQ) were carried out among NuAge study participants as part of the Canadian Longitudinal Study on Aging (CLSA) Phase II validation studies, CIHR 2006-2008. The NuAge study was supported by the Canadian Institutes for Health Research (CIHR), Grant number MOP-62842, and the Quebec Network for Research on Aging, a network funded by the Fonds de Recherche du Québec-Santé. The opinions expressed in this manuscript are the authors’ own and do not reflect the views of the Canadian Longitudinal Study on Aging.

Conflicts of Interest

The authors declare that they have no competing interests.

Abbreviations

The following abbreviations are used in this manuscript:
CLSACanadian Longitudinal Study on Aging
FVfruit and vegetable
F/Vfruit or vegetable
BMIbody mass index
SDQShort Dietary Questionnaire
FFQfood frequency questionnaire
GDPgross domestic product
ORodds ratios
CI9595% confidence intervals

References

  1. World Health Organization. Ageing and Health. 2022. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health (accessed on 12 June 2024).
  2. Statistics Canada. Seniors. Available online: https://www150.statcan.gc.ca/n1/pub/11-402-x/2011000/chap/seniors-aines/seniors-aines-eng.htm (accessed on 12 June 2024).
  3. Lee, H.Y.; Jang, S.N.; Lee, S.; Cho, S.I.; Park, E.O. The relationship between social participation and self-rated health by sex and age: A cross-sectional survey. Int. J. Nurs. Stud. 2008, 45, 1042–1054. [Google Scholar] [CrossRef] [PubMed]
  4. Keefe, J.; Fancey, P.; Andrew, M.; Hall, M. A Profile of Social Isolation in Canada. Prepared for Federal/Provincial/Territorial Committee of Officials (Seniors). 2006. Available online: https://www.canada.ca/en/national-seniors-council/programs/publications-reports/2014/social-isolation-seniors/page03.html (accessed on 12 June 2024).
  5. International Federation of Aging. Current and Emerging Issues Facing Older Canadians; International Federation of Aging: Toronto, ON, Canada, 2012. [Google Scholar]
  6. Levasseur, M.; Richard, L.; Gauvin, L.; Raymond, E. Inventory and analysis of definitions of social participation found in the aging literature: Proposed taxonomy of social activities. Soc. Sci. Med. 2010, 71, 2141–2149. [Google Scholar] [CrossRef] [PubMed]
  7. Hämmig, O. Health risks associated with social isolation in general and in young, middle and old age. PLoS ONE 2019, 14, e0219663. [Google Scholar] [CrossRef]
  8. Holt-Lunstad, J.; Smith, T.B.; Layton, J.B. Social relationships and mortality risk: A meta-analytic review. PLoS Med. 2010, 7, e1000316. [Google Scholar] [CrossRef] [PubMed]
  9. Sowa, A.; Tobiasz-Adamczyk, B.; Topór-Mądry, R.; Poscia, A.; la Milia, D.I. Predictors of healthy ageing: Public health policy targets. BMC Health Serv. Res. 2016, 16, 289. [Google Scholar] [CrossRef]
  10. Gilmour, H. Social participation and the health and well-being of Canadian seniors. Health Rep. 2012, 23, 23–32. [Google Scholar]
  11. Holt-Lunstad, J.; Smith, T.B.; Baker, M.; Harris, T.; Stephenson, D. Loneliness and social isolation as risk factors for mortality: A meta-analytic review. Perspect. Psychol. Sci. 2015, 10, 227–237. [Google Scholar] [CrossRef]
  12. Gilmour, H.; Ramage-Morin, P.L. Social isolation and mortality among Canadian seniors. Health Rep. 2020, 31, 27–38. [Google Scholar] [CrossRef]
  13. Shankar, A.; McMunn, A.; Banks, J.; Steptoe, A. Loneliness, social isolation, and behavioral and biological health indicators in older adults. Health Psychol. 2011, 30, 377–385. [Google Scholar] [CrossRef]
  14. Delerue Matos, A.; Barbosa, F.; Cunha, C.; Voss, G.; Correia, F. Social isolation, physical inactivity and inadequate diet among European middle-aged and older adults. BMC Public Health 2021, 21, 924. [Google Scholar] [CrossRef]
  15. Menec, V.H.; Newall, N.E.; Mackenzie, C.S.; Shooshtari, S.; Nowicki, S. Examining individual and geographic factors associated with social isolation and loneliness using Canadian Longitudinal Study on Aging (CLSA) data. PLoS ONE 2019, 14, e0211143. [Google Scholar] [CrossRef]
  16. Valtorta, N.K.; Kanaan, M.; Gilbody, S.; Hanratty, B. Loneliness, social isolation and risk of cardiovascular disease in the English Longitudinal Study of Ageing. Eur. J. Prev. Cardiol. 2018, 25, 1387–1396. [Google Scholar] [CrossRef]
  17. Yang, Y.C.; Boen, C.; Gerken, K.; Li, T.; Schorpp, K.; Harris, K.M. Social relationships and physiological determinants of longevity across the human life span. Proc. Natl. Acad. Sci. USA 2016, 113, 578–583. [Google Scholar] [CrossRef]
  18. Schram, M.T.; Assendelft, W.J.J.; van Tilburg, T.G.; Dukers-Muijrers, N. Social networks and type 2 diabetes: A narrative review. Diabetologia 2021, 64, 1905–1916. [Google Scholar] [CrossRef]
  19. Holt-Lunstad, J.; Robles, T.F.; Sbarra, D.A. Advancing social connection as a public health priority in the United States. Am. Psychol. 2017, 72, 517–530. [Google Scholar] [CrossRef]
  20. Hosseini, Z.; Veenstra, G.; Khan, N.A.; Conklin, A.I. Social connections and hypertension in women and men: A population-based cross-sectional study of the Canadian Longitudinal Study on Aging. J. Hypertens. 2021, 39, 651–660. [Google Scholar] [CrossRef] [PubMed]
  21. Hosseini, Z.; Veenstra, G.; Khan, N.A.; Conklin, A.I. Associations between social connections, their interactions, and obesity differ by gender: A population-based, cross-sectional analysis of the Canadian Longitudinal Study on Aging. PLoS ONE 2020, 15, e0235977. [Google Scholar] [CrossRef] [PubMed]
  22. Hosseini, Z.; Safari, A.; Khan, N.A.; Veenstra, G.; Conklin, A.I. Adiposity and the role of diverse social supports: An observational, gender-sensitive study using the baseline Canadian Longitudinal Study on Aging. Public Health Nutr. 2021, 24, 6103–6112. [Google Scholar] [CrossRef] [PubMed]
  23. Hosseini, Z.; Safari, A.; Khan, N.A.; Veenstra, G.; Conklin, A.I. Gender Differences in the Role of Social Support for Hypertension Prevention in Canada: A Population-Based Cross-Sectional Study of the Canadian Longitudinal Study on Aging Cohort. CJC Open 2021, 3, S62–S70. [Google Scholar] [CrossRef]
  24. 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]
  25. Hanna, K.; Cross, J.; Nicholls, A.; Gallegos, D. The association between loneliness or social isolation and food and eating behaviours: A scoping review. Appetite 2023, 191, 107051. [Google Scholar] [CrossRef]
  26. Higgs, S.; Thomas, J. Social influences on eating. Curr. Opin. Behav. Sci. 2016, 9, 1–6. [Google Scholar] [CrossRef]
  27. Saif, N.T.; Addison, O.R.; Hughes Barry, K.; Falvey, J.R.; Parker, E.A. Associations between social isolation and diet quality among US adults with disability participating in the National Health and Nutrition Examination Survey, 2013–2018. Prev. Med. Rep. 2023, 36, 102413. [Google Scholar] [CrossRef] [PubMed]
  28. Kalousova, L. Social isolation as a risk factor for inadequate diet of older Eastern Europeans. Int. J. Public Health 2014, 59, 707–714. [Google Scholar] [CrossRef]
  29. Kobayashi, L.C.; Steptoe, A. Social Isolation, Loneliness, and Health Behaviors at Older Ages: Longitudinal Cohort Study. Ann. Behav. Med. 2018, 52, 582–593. [Google Scholar] [CrossRef] [PubMed]
  30. Steptoe, A.; Fong, H.L.; Lassale, C. Social isolation, loneliness and low dietary micronutrient intake amongst older people in England. Age Ageing 2024, 53, afae223. [Google Scholar] [CrossRef]
  31. Conklin, A.I.; Forouhi, N.G.; Surtees, P.; Khaw, K.T.; Wareham, N.J.; Monsivais, P. Social relationships and healthful dietary behaviour: Evidence from over-50s in the EPIC cohort, UK. Soc. Sci. Med. 2014, 100, 167–175. [Google Scholar] [CrossRef]
  32. Mills, C.M.; Keller, H.H.; DePaul, V.G.; Donnelly, C. Social Network Factors Affect Nutrition Risk in Middle-Aged and Older Adults: Results from the Canadian Longitudinal Study on Aging. J. Nutr. Health Aging 2023, 27, 46–58. [Google Scholar] [CrossRef]
  33. Bloom, I.; Edwards, M.; Jameson, K.A.; Syddall, H.E.; Dennison, E.; Gale, C.R.; Baird, J.; Cooper, C.; Aihie Sayer, A.; Robinson, S. Influences on diet quality in older age: The importance of social factors. Age Ageing 2017, 46, 277–283. [Google Scholar] [CrossRef]
  34. Nishio, M.; Takagi, D.; Shinozaki, T.; Kondo, N. Community social networks, individual social participation and dietary behavior among older Japanese adults: Examining mediation using nonlinear structural equation models for three-wave longitudinal data. Prev. Med. 2021, 149, 106613. [Google Scholar] [CrossRef]
  35. Mehranfar, S.; Madani Civi, R.; Plunkett, R.; Murphy, R.A.; Cohen, T.R.; Conklin, A.I. The Longitudinal Evidence on Social Ties and Fruit and Vegetable Intake among Aging Adults: A Systematic Review. Nutr. Rev. 2024, nuae134. [Google Scholar] [CrossRef] [PubMed]
  36. Feraco, A.; Armani, A.; Amoah, I.; Guseva, E.; Camajani, E.; Gorini, S.; Strollo, R.; Padua, E.; Caprio, M.; Lombardo, M. Assessing gender differences in food preferences and physical activity: A population-based survey. Front. Nutr. 2024, 11, 1348456. [Google Scholar] [CrossRef] [PubMed]
  37. Naud, D.; Généreux, M.; Alauzet, A.; Bruneau, J.F.; Cohen, A.; Levasseur, M. Social participation and barriers to community activities among middle-aged and older Canadians: Differences and similarities according to gender and age. Geriatr. Gerontol. Int. 2021, 21, 77–84. [Google Scholar] [CrossRef] [PubMed]
  38. Raina, P.; Wolfson, C.; Kirkland, S.; Griffith, L.E.; Balion, C.; 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. J. Epidemiol. 2019, 48, 1752–1753j. [Google Scholar] [CrossRef]
  39. Raina, P.S.; 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 2009, 28, 221–229. [Google Scholar] [CrossRef]
  40. Raina, P.; Wolfson, C.; Kirkland, S. Canadian Longitudinal Study on Aging (CLSA) Protocol Version 3.0. 2008. Available online: https://www.clsa-elcv.ca/researchers (accessed on 26 April 2024).
  41. 2023 CLSA Sampling and Computation of Response Rates and Sample Weights for the Tracking (Telephone Interview) Participants and Comprehensive Participants (Version 2.0). Available online: https://www.clsa-elcv.ca/our-resources/2023-sampling-and-computation-of-response-rates-and-sample-weights-for-the-tracking-telephone-interview-participants-and-comprehensive-participants/ (accessed on 26 April 2024).
  42. Lachat, C.; Hawwash, D.; Ocké, M.C.; Berg, C.; Forsum, E.; Hörnell, A.; Larsson, C.; Sonestedt, E.; Wirfält, E.; Åkesson, A.; et al. Strengthening the Reporting of Observational Studies in Epidemiology-Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement. PLoS Med. 2016, 13, e1002036. [Google Scholar] [CrossRef]
  43. Shatenstein, B.; Payette, H. Evaluation of the Relative Validity of the Short Diet Questionnaire for Assessing Usual Consumption Frequencies of Selected Nutrients and Foods. Nutrients 2015, 7, 6362–6374. [Google Scholar] [CrossRef]
  44. Canada’s Food Guide. Eat Vegetables and Fruits. Available online: https://food-guide.canada.ca/en/healthy-eating-recommendations/make-it-a-habit-to-eat-vegetables-fruit-whole-grains-and-protein-foods/eat-vegetables-and-fruits/ (accessed on 20 March 2024).
  45. NIH National Cancer Institute. Identifying Extreme Exposure Values. 2021. Available online: https://epi.grants.cancer.gov/nhanes/dietscreen/scoring/current/identify.html (accessed on 20 March 2024).
  46. Ceolin, G.; Veenstra, G.; Mehranfar, S.; Civi, R.M.; Khan, N.A.; Conklin, A.I. Trends, transitions and patterning in social activity over time among aging women and men: A secondary analysis of the Canadian Longitudinal Study on Aging (CLSA). Arch. Gerontol. Geriatr. 2024, 128, 105618. [Google Scholar] [CrossRef]
  47. Textor, J.; van der Zander, B.; Gilthorpe, M.S.; Liśkiewicz, M.; Ellison, G.T. Robust causal inference using directed acyclic graphs: The R package ‘dagitty’. Int. J. Epidemiol. 2017, 45, 1887–1894. [Google Scholar] [CrossRef]
  48. Ding, D.; Gale, J.; Bauman, A.; Phongsavan, P.; Nguyen, B. Effects of divorce and widowhood on subsequent health behaviours and outcomes in a sample of middle-aged and older Australian adults. Sci. Rep. 2021, 11, 15237. [Google Scholar] [CrossRef]
  49. Eng, P.M.; Kawachi, I.; Fitzmaurice, G.; Rimm, E.B. Effects of marital transitions on changes in dietary and other health behaviours in US male health professionals. J. Epidemiol. Community Health 2005, 59, 56–62. [Google Scholar] [CrossRef] [PubMed]
  50. Lee, S.; Cho, E.; Grodstein, F.; Kawachi, I.; Hu, F.B.; Colditz, G.A. Effects of marital transitions on changes in dietary and other health behaviours in US women. Int. J. Epidemiol. 2005, 34, 69–78. [Google Scholar] [CrossRef]
  51. Noguchi, T.; Kondo, F.; Nishiyama, T.; Otani, T.; Nakagawa-Senda, H.; Watanabe, M.; Imaeda, N.; Goto, C.; Hosono, A.; Shibata, K.; et al. The Impact of Marital Transitions on Vegetable Intake in Middle-aged and Older Japanese Adults: A 5-year Longitudinal Study. J. Epidemiol. 2022, 32, 89–95. [Google Scholar] [CrossRef] [PubMed]
  52. Plessz, M.; Gueguen, A. Who Benefits from Living in a Couple? A Longitudinal Study of Eating Practices at the Intersection of Gender, Conjugal Situation, and Social Status. Rev. Fr. Sociol. (Engl. Ed.) 2017, 58, 1–29. [Google Scholar]
  53. Vinther, J.L.; Conklin, A.I.; Wareham, N.J.; Monsivais, P. Marital transitions and associated changes in fruit and vegetable intake: Findings from the population-based prospective EPIC-Norfolk cohort, UK. Soc. Sci. Med. 2016, 157, 120–126. [Google Scholar] [CrossRef] [PubMed]
  54. Wilcox, S.; Evenson, K.R.; Aragaki, A.; Wassertheil-Smoller, S.; Mouton, C.P.; Loevinger, B.L. The effects of widowhood on physical and mental health, health behaviors, and health outcomes: The Women’s Health Initiative. Health Psychol. 2003, 22, 513–522. [Google Scholar] [CrossRef]
  55. Ikram, M.A. The disjunctive cause criterion by VanderWeele: An easy solution to a complex problem? Eur. J. Epidemiol. 2019, 34, 223–224. [Google Scholar] [CrossRef]
  56. Pollack, C.E.; Chideya, S.; Cubbin, C.; Williams, B.; Dekker, M.; Braveman, P. Should health studies measure wealth? A systematic review. Am. J. Prev. Med. 2007, 33, 250–264. [Google Scholar] [CrossRef]
  57. Tannenbaum, C.; Greaves, L.; Graham, I.D. Why sex and gender matter in implementation research. BMC Med. Res. Methodol. 2016, 16, 145. [Google Scholar] [CrossRef]
  58. STATA. Melogit—Multilevel Mixed-Effects Logistic Regression. Available online: https://www.stata.com/manuals13/memelogit.pdf (accessed on 28 April 2024).
  59. Bassim, C.; Mayhew, A.J.; Ma, J.; Kanters, D.; Verschoor, C.P.; Griffith, L.E.; Raina, P. Oral Health, Diet, and Frailty at Baseline of the Canadian Longitudinal Study on Aging. J. Am. Geriatr. Soc. 2020, 68, 959–966. [Google Scholar] [CrossRef]
  60. Palaniappan, U.; Starkey, L.J.; O’Loughlin, J.; Gray-Donald, K. Fruit and Vegetable Consumption Is Lower and Saturated Fat Intake Is Higher among Canadians Reporting Smoking. J. Nutr. 2001, 131, 1952–1958. [Google Scholar] [CrossRef] [PubMed]
  61. Kesse, E.; Clavel-Chapelon, F.; Slimani, N.; van Liere, M. Do eating habits differ according to alcohol consumption? Results of a study of the French cohort of the European Prospective Investigation into Cancer and Nutrition (E3N-EPIC)1234. Am. J. Clin. Nutr. 2001, 74, 322–327. [Google Scholar] [CrossRef] [PubMed]
  62. Marino, M.; Masella, R.; Bulzomi, P.; Campesi, I.; Malorni, W.; Franconi, F. Nutrition and human health from a sex–gender perspective. Mol. Asp. Med. 2011, 32, 1–70. [Google Scholar] [CrossRef]
  63. Lamarche, B.; Brassard, D.; Lapointe, A.; Laramée, C.; Kearney, M.; Côté, M.; Bélanger-Gravel, A.; Desroches, S.; Lemieux, S.; Plante, C. Changes in diet quality and food security among adults during the COVID-19-related early lockdown: Results from NutriQuébec. Am. J. Clin. Nutr. 2021, 113, 984–992. [Google Scholar] [CrossRef]
  64. Payette, H.; Shatenstein, B. Determinants of healthy eating in community-dwelling elderly people. Can. J. Public Health 2005, 96 (Suppl. S3), S30–S35. [Google Scholar] [CrossRef]
  65. Conklin, A.I.; Forouhi, N.G.; Surtees, P.; Wareham, N.J.; Monsivais, P. Gender and the double burden of economic and social disadvantages on healthy eating: Cross-sectional study of older adults in the EPIC-Norfolk cohort. BMC Public Health 2015, 15, 692. [Google Scholar] [CrossRef]
  66. Wister, A.; Cosco, T.; Mitchell, B.; Menec, V.; Fyffe, I. Development and Concurrent Validity of a Composite Social Isolation Index for Older Adults Using the CLSA. Can. J. Aging 2019, 38, 180–192. [Google Scholar] [CrossRef]
  67. Barr, E.; Popkin, R.; Roodzant, E.; Jaworski, B.; Temkin, S.M. Gender as a social and structural variable: Research perspectives from the National Institutes of Health (NIH). Transl. Behav. Med. 2024, 14, 13–22. [Google Scholar] [CrossRef]
  68. Conklin, A.I. Gender, Diet Quality and Obesity: Economic and Social Determinants, and Their Interactions, in Older Adults. 2014. Available online: https://www.repository.cam.ac.uk/handle/1810/262808 (accessed on 12 June 2024).
  69. Statistics Canada. The Economic Well-Being of Women in Canada. Available online: https://www150.statcan.gc.ca/n1/en/pub/89-503-x/2015001/article/54930-eng.pdf?st=iLTNjJ0a (accessed on 23 September 2024).
  70. Leopold, T. Gender Differences in the Consequences of Divorce: A Study of Multiple Outcomes. Demography 2018, 55, 769–797. [Google Scholar] [CrossRef]
  71. Tucker, J.S. Health-Related Social Control Within Older Adults’ Relationships. J. Gerontol. Ser. B 2002, 57, P387–P395. [Google Scholar] [CrossRef]
  72. Emmons, K.M.; Barbeau, E.M.; Gutheil, C.; Stryker, J.E.; Stoddard, A.M. Social Influences, Social Context, and Health Behaviors Among Working-Class, Multi-Ethnic Adults. Health Educ. Behav. 2007, 34, 315–334. [Google Scholar] [CrossRef] [PubMed]
  73. Zheng, T. Gambling and Drinking, Do They Go Hand in Hand? The Impact of Drinking on Gambling Behaviors and Game Outcomes in Texas Hold’em Poker Short Game. In New Perspectives and Paradigms in Applied Economics and Business; Springer International Publishing: Cham, Switzerland, 2023; pp. 219–251. [Google Scholar]
  74. Government of Canada Publications. Sport Participation 2010. Available online: https://publications.gc.ca/collections/collection_2013/pc-ch/CH24-1-2012-eng.pdf (accessed on 4 October 2024).
  75. Courtney, J.; Handley, E.; Pagoto, S.; Russell, M.; Conroy, D.E. Alcohol use as a function of physical activity and golfing motives in a national sample of United States golfers. Nutrients 2021, 13, 1856. [Google Scholar] [CrossRef] [PubMed]
  76. Reynolds, K.A.; Sommer, J.; Mackenzie, C.S.; Koven, L. A Profile of Social Participation in a Nationally Representative Sample of Canadian Older Adults: Findings from the Canadian Longitudinal Study on Aging. Can. J. Aging 2022, 41, 505–513. [Google Scholar] [CrossRef] [PubMed]
  77. Ramage-Morin, P.L.; Garriguet, D. Nutritional risk among older Canadians. Health Rep. 2013, 24, 3–13. [Google Scholar]
  78. Holt-Lunstad, J. National Health Guidelines for Social Connection: What Is the Evidence in Support and What Might the Guidelines Say? Policy Insights Behav. Brain Sci. 2023, 10, 41–50. [Google Scholar] [CrossRef]
  79. Cohn-Schwartz, E.; Schmitz, A. From attitudes to social networks: National gender-role attitudes and gender differences in late-life social relationships. Soc. Netw. 2024, 76, 79–87. [Google Scholar] [CrossRef]
  80. Shin, H.; Park, C. Gender differences in social networks and physical and mental health: Are social relationships more health protective in women than in men? Front. Psychol. 2023, 14, 1216032. [Google Scholar] [CrossRef]
  81. Bedrov, A.; Gable, S.L. Thriving together: The benefits of women’s social ties for physical, psychological and relationship health. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2023, 378, 20210441. [Google Scholar] [CrossRef] [PubMed]
  82. Finkel, D.; Andel, R.; Pedersen, N.L. Gender Differences in Longitudinal Trajectories of Change in Physical, Social, and Cognitive/Sedentary Leisure Activities. J. Gerontol. Ser. B 2016, 73, 1491–1500. [Google Scholar] [CrossRef]
  83. DeVault, M. Conflict and Deference. In Feeding the Family; University of Chicago Press: Chicago, IL, USA, 1991; pp. 138–166. [Google Scholar]
  84. Taylor, S.E.; Klein, L.C.; Lewis, B.P.; Gruenewald, T.L.; Gurung, R.A.; Updegraff, J.A. Biobehavioral responses to stress in females: Tend-and-befriend, not fight-or-flight. Psychol. Rev. 2000, 107, 411–429. [Google Scholar] [CrossRef] [PubMed]
  85. Edwards, O.; Mawani, A. Healthy Aging in Canada: A New Vision, a Vital Investment from Evidence to Action; Public Health Agency of Canada: Ottawa, ON, Canada, 2006.
  86. Hill, A.B. The environment and disease: Association or causation? Proc. R. Soc. Med. 1965, 58, 295–300. [Google Scholar] [CrossRef] [PubMed]
Nutrients 17 01005 g001
Figure 1. Average predicted probability of non-daily vegetable intake associated with social isolation transitions and social participation transitions among women and men in the Canadian Longitudinal Study on Aging (2011–2021). Dash line, men; solid line, women. (A), social isolation transitions; (B), social participation transitions. The social isolation transition variable is classified as: (1) remained isolated (no change in 0 or 1 activity/month at both waves); (2) remained not isolated (no change in 2 or more activities/month at both waves); (3) became isolated (transition to 0 or 1 activity/month); and (4) became not isolated (transition to 2 or more activities/month). Social participation transitions are classified as: (1) remained diverse (no change in ≥5 activities/month at both waves); (2) remained less diverse (no change in <5 activities/month at both waves); (3) became less diverse (transition from ≥5 activities to <5 activities/month); and (4) became diverse (transition from <5 activities to ≥5 activities/month).
Figure 1. Average predicted probability of non-daily vegetable intake associated with social isolation transitions and social participation transitions among women and men in the Canadian Longitudinal Study on Aging (2011–2021). Dash line, men; solid line, women. (A), social isolation transitions; (B), social participation transitions. The social isolation transition variable is classified as: (1) remained isolated (no change in 0 or 1 activity/month at both waves); (2) remained not isolated (no change in 2 or more activities/month at both waves); (3) became isolated (transition to 0 or 1 activity/month); and (4) became not isolated (transition to 2 or more activities/month). Social participation transitions are classified as: (1) remained diverse (no change in ≥5 activities/month at both waves); (2) remained less diverse (no change in <5 activities/month at both waves); (3) became less diverse (transition from ≥5 activities to <5 activities/month); and (4) became diverse (transition from <5 activities to ≥5 activities/month).
Nutrients 17 01005 g001
Nutrients 17 01005 g002
Figure 2. Average predicted probability of non-daily fruit intake associated with social isolation transitions and social participation transitions among women and men in the Canadian Longitudinal Study on Aging (2011–2021). Dash line, men; solid line, women. (A), social isolation transitions; (B), social participation transitions. The social isolation transition variable is classified as: (1) remained isolated (no change in 0 or 1 activity/month at both waves); (2) remained not isolated (no change in 2 or more activities/month at both waves); (3) became isolated (transition to 0 or 1 activity/month); and (4) became not isolated (transition to 2 or more activities/month). Social participation transitions are classified as: (1) remained diverse (no change in ≥5 activities/month at both waves); (2) remained less diverse (no change in <5 activities/month at both waves); (3) became less diverse (transition from ≥5 activities to <5 activities/month); and (4) became diverse (transition from <5 activities to ≥5 activities/month).
Figure 2. Average predicted probability of non-daily fruit intake associated with social isolation transitions and social participation transitions among women and men in the Canadian Longitudinal Study on Aging (2011–2021). Dash line, men; solid line, women. (A), social isolation transitions; (B), social participation transitions. The social isolation transition variable is classified as: (1) remained isolated (no change in 0 or 1 activity/month at both waves); (2) remained not isolated (no change in 2 or more activities/month at both waves); (3) became isolated (transition to 0 or 1 activity/month); and (4) became not isolated (transition to 2 or more activities/month). Social participation transitions are classified as: (1) remained diverse (no change in ≥5 activities/month at both waves); (2) remained less diverse (no change in <5 activities/month at both waves); (3) became less diverse (transition from ≥5 activities to <5 activities/month); and (4) became diverse (transition from <5 activities to ≥5 activities/month).
Nutrients 17 01005 g002
Table 1. Sample characteristics across social isolation transitions among aging women and men in the eligible sample consuming daily vegetable intake in the Canadian Longitudinal Study on Aging (2011–2021).
Table 1. Sample characteristics across social isolation transitions among aging women and men in the eligible sample consuming daily vegetable intake in the Canadian Longitudinal Study on Aging (2011–2021).
Social Isolation Transitions Mean (SD) Age (Years)Highest Education 1Highest Household Income 2Home-OwnerUrban LocationBMI (kg/m2)No Chronic ConditionNot
Sleep Deprived		Non-Daily Vegetable Intake
Women
Remained not isolated (n = 8615)59.09 (9.83)6937 (67.5%)1390 (17.1%)7524 (87.4%)7729 (92.2%)27.52 (5.89)463 (6.6%)5503 (63.0%)1532 (16.1%)
Remained isolated (n = 163)63.56 (10.42)98 (39.7%)8
(9.9%)		110 (67.6%)142 (93.2%)28.05 (5.78)8 (4.4%)94 (51.1%)53 (27.7%)
Became isolated (n = 366)59.72 (11.18)250 (51.8%)34
(8.5%)		281 (77.2%)324 (94.1%)28.90 (6.84)9 (1.5%)197 (54.7%)84 (26.1%)
Became not isolated (n = 257)58.50 (8.7)162 (47.7%)25
(9.2%)		192 (70.1%)233 (95.1%)28.20 (6.72)14 (14.5%)149 (54.4%)66 (19.1%)
Men
Remained not isolated (n = 5907)58.82 (9.92)4989 (70.9%)1371 (23.4%)5341 (88.0%)5330 (94.4%)27.92 (4.55)429 (9.2%)3877 (64.2%)1754 (27.7%)
Remained isolated (n = 181)61.77 (9.06)130 (62.0%)14
(5.9%)		145 (78.0%)164 (93.0%)28.89 (5.79)5 (2.6%)103 (59.8%)66 (29.4%)
Became isolated (n = 292)59.12 (8.79)234 (68.2%)41 (18.9%)236 (75.9%)258 (95.7%)28.19 (5.65)26 (9.1%)181 (64.6%)102 (34.6%)
Became not isolated (n = 218)58.63 (10.62)159 (49.8%)30
(9.6%)		177 (75.1%)191 (93.9%)28.96 (5.30)14 (3.6%)136 (55.7%)84 (43.9%)
Descriptive statistics, including percentages (%) and means (SD), were calculated using CLSA survey inflation weights. All variables were reported at baseline, except for vegetable intake at follow-up 2. 1 The highest education level was post-secondary graduation [university degree]. 2 The highest income was ≥CAD 150,000.
Table 2. Sample characteristics across social participation transitions among aging women and men in the eligible sample consuming daily vegetable intake in the Canadian Longitudinal Study on Aging (2011–2021).
Table 2. Sample characteristics across social participation transitions among aging women and men in the eligible sample consuming daily vegetable intake in the Canadian Longitudinal Study on Aging (2011–2021).
Social Participation TransitionsAge (Years)Highest Education 1Highest Household Income 2Home-OwnerUrban LocationBMI (kg/m2)No Chronic ConditionNot
Sleep Deprived		Non-Daily Vegetable Intake
Women
Remained diverse (n = 3152)60.68 (10.18)2675 (74.7%)521 (18.9%)2817 (89.8%)2856 (92.6%)27.36 (5.75)160 (6.6%)2073 (66.9%)503 (14.6%)
Remained less diverse (n = 3857)58.43 (9.7)2852 (61.1%)574 (15.1%)3228 (82.7%)3406 (92.1%)27.89 (6.18)207 (7.2%)2353 (57.9%)809 (19.7%)
Became less diverse (n = 1294)58.63 (10.09)1035 (62.0%)192 (15.0%)1105 (87.9%)1173 (91.7%)27.61 (5.9)70 (4.7%)804 (63.2%)228 (15.0%)
Became diverse (n = 1098)59.91 (9.44)885 (65.3%)170 (16.2%)957 (86.5%)993 (94.2%)27.15 (5.6)57 (6.1%)713 (66.7%)195 (14.2%)
Men
Remained diverse (n = 1894)60.29 (10.49)1661 (74.5%)459 (25.4%)1717 (90.0%)1716 (93.6%)28.73 (4.89)137 (9.7%)1258 (63.3%)515 (23.2%)
Remained less diverse (n = 3145)58.08 (9.39)2523 (65.5%)634 (20.6%)2752 (83.0%)2810 (94.5%)27.63 (4.59)235 (9.0%)2012 (63.1%)1040 (33.1%)
Became less diverse (n = 832)59.28 (10.37)704 (71.7%)208 (21.7%)757 (90.8%)756 (94.4%)28.15 (4.53)48 (6.4%)529 (68.1%)231 (22.5%)
Became diverse (n = 727)59.53 (9.97)624 (76.1%)155 (21.7%)673 (91.0%)661 (95.7%)28.11 (4.72)54 (8.3%)498 (62.1%)220 (28.1%)
Descriptive statistics, including percentages (%) and means (SD), were calculated using CLSA survey inflation weights. All variables were reported at baseline, except for vegetable intake at follow-up 2. 1 The highest education level was post-secondary graduation [university degree]. 2 The highest income was ≥CAD 150,000.
Table 3. Gender-specific odds ratios (95% CI) of non-daily vegetable intake associated with social isolation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Table 3. Gender-specific odds ratios (95% CI) of non-daily vegetable intake associated with social isolation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Model AModel BModel CModel D
ORCI95ORCI95ORCI95ORCI95
Women
Remained not isolatedRef Ref Ref Ref
Remained isolated 2.10 ***[1.51, 2.94]1.85 ***[1.32, 2.59]1.85 ***[1.31, 2.59]1.85 ***[1.32, 2.59]
Became isolated 1.32 *[1.02, 1.69]1.20[0.93, 1.55]1.19[0.93, 1.54]1.19[0.93, 1.54]
Became not isolated1.50 **[1.13, 2.01]1.36 *[1.02, 1.82]1.37 *[1.03, 1.84]1.38 *[1.03, 1.84]
Men
Remained not isolatedRef Ref Ref Ref
Remained isolated 1.32[0.97, 1.80]1.16[0.85, 1.59]1.14[0.83, 1.56]1.14[0.83, 1.56]
Became isolated 1.25[0.98, 1.61]1.16[0.90, 1.50]1.16[0.90, 1.49]1.16[0.90, 1.49]
Became not isolated1.42 *[1.07, 1.88]1.32[0.99, 1.75]1.31[0.99, 1.75]1.31[0.99, 1.74]
Gender-specific odds ratios (95% CIs) obtained by random coefficient logistic regression with an interaction term (social isolation and gender) on the sample with daily intake at baseline conditioning on confounders. Model A: Follow-up time, age, BMI, and chronic condition (n = 16,080). Model B: Model A + education, income, home ownership, and geographic location (n = 16,034). Model C: Model B + sleep deprivation (n = 15,999). Model D: Model C + provincial gross domestic product (GDP), % provincial food insecurity, vegetable consumer price index (CPI), and provincial public spending (n = 15,999). * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 4. Gender-specific odds ratios (95% CI) of non-daily vegetable intake associated with social participation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Table 4. Gender-specific odds ratios (95% CI) of non-daily vegetable intake associated with social participation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Model AModel BModel CModel D
ORCI95ORCI95ORCI95ORCI95
Women
Remained diverse Ref Ref Ref Ref
Remained less diverse 1.36 *[1.20, 1.54]1.29 *[1.14, 1.47]1.30 *[1.15, 1.48]1.30 *[1.15, 1.47]
Became less diverse1.12[0.94, 1.33]1.08[0.91, 1.29]1.08[0.91, 1.29]1.08[0.91, 1.29]
Became diverse 1.12[0.93, 1.34]1.09[0.91, 1.31]1.10[0.91, 1.32]1.10[0.91, 1.32]
Men
Remained diverse Ref Ref Ref Ref
Remained less diverse 1.36 *[1.20, 1.55]1.29 *[1.13, 1.46]1.29 *[1.13, 1.46]1.29 *[1.13, 1.46]
Became less diverse1.04[0.87, 1.25]1.03[0.85, 1.24]1.03[0.85, 1.24]1.03[0.85, 1.24]
Became diverse 1.18[0.98, 1.43]1.15[0.95, 1.40]1.16[0.95, 1.40]1.16[0.95, 1.4]
Gender-specific odds ratios (95% CIs) obtained by random coefficient logistic regression with an interaction term (changes in diversity of social participation and gender) on the sample with daily intake at baseline conditioning on confounders. Model A: Follow-up time, age, BMI, and chronic condition (n = 16,080). Model B: Model A + education, income, home ownership, and geographic location (n = 16,034). Model C: Model B + sleep deprivation (n = 15,999). Model D: Model C + provincial gross domestic product (GDP, % provincial food insecurity, vegetable consumer price index (CPI), and public spending (n = 15,999). * p < 0.001.
Table 5. Gender-specific odds ratios (95% CI) of non-daily fruit intake associated with social isolation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Table 5. Gender-specific odds ratios (95% CI) of non-daily fruit intake associated with social isolation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Model AModel BModel CModel D
ORCI95ORCI95ORCI95ORCI95
Women
Remained not isolatedRef Ref Ref Ref
Remained isolated 2.42 **[1.72, 3.40]2.19 **[1.56, 3.08]2.23 **[1.58, 3.14]2.23 **[1.58, 3.14]
Became isolated 1.19[0.90, 1.57]1.14[0.86, 1.50]1.14[0.86, 1.51]1.14[0.86, 1.51]
Became not isolated1.88 **[1.41, 2.51]1.76 **[1.32, 2.35]1.77 **[1.33, 2.37]1.77 **[1.33, 2.37]
Men
Remained not isolatedRef Ref Ref Ref
Remained isolated 1.46 *[1.06, 2.01]1.35[0.98, 1.87]1.35[0.98, 1.86]1.35[0.98, 1.86]
Became isolated 1.32 *[1.03, 1.68]1.28[1.00, 1.63]1.27[1.00, 1.63]1.27[1.00, 1.63]
Became not isolated1.21[0.91, 1.62]1.17[0.88, 1.56]1.17[0.88, 1.57]1.17[0.88, 1.57]
Gender-specific odds ratios (95% CIs) obtained by random coefficient logistic regression with an interaction term (social isolation and gender) on the sample with daily intake at baseline conditioning on confounders. Model A: Follow-up time, age, BMI, and chronic condition (n = 18,494). Model B: Model A + education, income, home ownership, and geographic location (n = 18,448). Model C: Model B + sleep deprivation (n = 18,413). Model D: Model C + provincial gross domestic product (GDP), % provincial food insecurity, fruit consumer price index (CPI), and provincial public spending (n = 18,413). * p < 0.05; ** p < 0.001.
Table 6. Gender-specific odds ratios (95% CI) of non-daily fruit intake associated with social participation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Table 6. Gender-specific odds ratios (95% CI) of non-daily fruit intake associated with social participation transitions in the Canadian Longitudinal Study on Aging (2011–2021).
Model AModel BModel CModel D
ORCI95ORCI95ORCI95ORCI95
Women
Remained diverse Ref Ref Ref Ref
Remained less diverse 1.71 ***[1.49, 1.96]1.63 ***[1.42, 1.87]1.64 ***[1.43, 1.88]1.64 ***[1.43, 1.88]
Became less diverse1.37 **[1.14, 1.65]1.34 **[1.12, 1.62]1.35 **[1.12, 1.62]1.35 **[1.12, 1.62]
Became diverse 1.24 *[1.01, 1.51]1.21[0.99, 1.48]1.22[1.00, 1.49]1.22[1.00, 1.49]
Men
Remained diverse Ref Ref Ref Ref
Remained less diverse 1.32 ***[1.16, 1.51]1.28 ***[1.12, 1.46]1.28 ***[1.12, 1.46]1.28 ***[1.12, 1.46]
Became less diverse1.07[0.89, 1.30]1.07[0.88, 1.29]1.07[0.88, 1.29]1.07[0.88, 1.29]
Became diverse 1.10[0.90, 1.34]1.08[0.88, 1.32]1.07[0.87, 1.30]1.07[0.87, 1.30]
Gender-specific odds ratios (95% CIs) obtained by random coefficient logistic regression with an interaction term (changes in diversity of social participation and gender) on the sample with daily intake at baseline conditioning on confounders. Model A: Follow-up time, age, BMI, and chronic condition (n = 18,494). Model B: Model A + education, income, home ownership, and geographic location (n = 18,448). Model C: Model B + sleep deprivation (n = 18,413). Model D: Model C + provincial gross domestic product (GDP, % provincial food insecurity, fruit consumer price index (CPI), and public spending (n = 18,413). * p < 0.05; ** p < 0.01; *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mehranfar, S.; Ceolin, G.; Madani Civi, R.; Keller, H.; Murphy, R.A.; Cohen, T.R.; Conklin, A.I. Gender, Adverse Changes in Social Engagement and Risk of Unhealthy Eating: A Prospective Cohort Study of the Canadian Longitudinal Study on Aging (2011–2021). Nutrients 2025, 17, 1005. https://doi.org/10.3390/nu17061005

AMA Style

Mehranfar S, Ceolin G, Madani Civi R, Keller H, Murphy RA, Cohen TR, Conklin AI. Gender, Adverse Changes in Social Engagement and Risk of Unhealthy Eating: A Prospective Cohort Study of the Canadian Longitudinal Study on Aging (2011–2021). Nutrients. 2025; 17(6):1005. https://doi.org/10.3390/nu17061005

Chicago/Turabian Style

Mehranfar, Sanaz, Gilciane Ceolin, Rana Madani Civi, Heather Keller, Rachel A. Murphy, Tamara R. Cohen, and Annalijn I. Conklin. 2025. "Gender, Adverse Changes in Social Engagement and Risk of Unhealthy Eating: A Prospective Cohort Study of the Canadian Longitudinal Study on Aging (2011–2021)" Nutrients 17, no. 6: 1005. https://doi.org/10.3390/nu17061005

APA Style

Mehranfar, S., Ceolin, G., Madani Civi, R., Keller, H., Murphy, R. A., Cohen, T. R., & Conklin, A. I. (2025). Gender, Adverse Changes in Social Engagement and Risk of Unhealthy Eating: A Prospective Cohort Study of the Canadian Longitudinal Study on Aging (2011–2021). Nutrients, 17(6), 1005. https://doi.org/10.3390/nu17061005

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop