Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Jul 15.
Published before final editing as: J Fam Psychol. 2026 Jul 13:10.1037/fam0001512. doi: 10.1037/fam0001512

Eating Together, Thriving Together: Using Common Fate Modeling to Understand Youth Well-Being

Mengyun Chen 1, Nicole M Froidevaux 2, Sunhye Bai 2, Jennifer P Agans 3
PMCID: PMC13367405  NIHMSID: NIHMS2181933  PMID: 42441644

Abstract

Family meals are widely recognized and studied as a cornerstone of youth well-being. However, the indirect associations linking family meals to youth well-being remain underexplored. The current cross-sectional study examined the family dinner frequency and family environment quality, and two primary youth outcomes: youth depressive symptoms and positive youth development. A total of 166 parent-youth dyads (Youth Mage = 15.2, SD = 1.35, 51% boys, 47% girls, and 2% nonbinary or transgender; Parent Mage = 41.2, SD = 4.83, 36% men, 63% women, and 1% nonbinary) living in the United States completed an online survey in the summer of 2021 or 2022. Using the common fate model, within the structural equation modeling framework, the current study incorporated reports from parents and youth on dinner frequency, family cohesion, and family conflict as dyad-level constructs to test the research aims and examine dyad-level processes. More frequent family dinners were associated with better youth well-being, as indicated by lower depressive symptoms (R2 = .186, β = −.33, p < .001) and higher positive youth development (R2 = .346, β = .50, p < .001). Moreover, the association between family dinner frequency and youth depressive symptoms was explained by family cohesion (β = −.23, 95% CI [−5.206, −1.276]) and family conflict (β = −.31, 95% CI [−8.710, −1.614]). Similarly, the association of family dinner frequency and positive youth development was explained by family cohesion (β = .25, 95% CI [0.096, 0.326]) and family conflict (β = .27, 95% CI [0.085, 0.501]). Findings suggest that efforts to encourage more frequent family dinners should prioritize not only the frequency of family interactions but also the quality of the interactions during time spent together.

Keywords: Family dinners, family processes, positive youth development, youth depressive symptoms, common fate model

Family meals provide a unique time and structure for family members to connect and engage with each other (e.g., Jones, 2018; for a review, see Harrison et al., 2015). They also naturally provide opportunities to socialize and to educate youth on eating-related habits, which have been associated with positive outcomes such as improved nutrition (for a review, see Fulkerson et al., 2014), stronger family functioning (e.g., White & Halliwell, 2010), and enhanced physical and psychological development throughout adolescence (for a review, see Harrison et al., 2015). However, few studies have examined indirect associations between family meals and youth well-being, especially positive youth development. Also, prior research on these constructs often relies on single-informant reports, despite constructs such as family meals being an experience shared by multiple family members. The common fate model (Ledermann & Kenny, 2012) helps to improve the precision and rigor of studies involving family-level constructs (e.g., meals, cohesion, conflict) by integrating each member’s reports into a single shared family-level latent construct (see Figure 1 for a standardized common fate model; Ledermann & Kenny, 2012). To address these gaps and advance family science, the current study uses common fate modeling to examine how family meals, particularly family dinner frequency, are associated with depressive symptoms and positive youth development, and whether these associations are explained by family environment quality (e.g., Belintxon et al., 2020; Jones, 2018; for a review, see Harrison et al., 2015).

Figure 1. The Standard Common Fate Model.

Figure 1.

Open in a new tab

Note. Figure adapted from Ledermann and Kenny (2012).

Youth Depressive Symptoms and Positive Youth Development

Marked by individual biological, cognitive, psychological, and social changes, adolescence is a developmental period when depression is highly prevalent (McLaughlin & King, 2015; for a review, see Lu, Lin, & Su, 2024). In the United States, approximately 20% of adolescents aged 12 to 17 experienced at least one major depressive episode, with a higher prevalence among females compared to males (National Institute of Mental Health, 2021). Depressive symptoms are associated with difficulties across multiple domains of adolescent functioning, including identity development, peer relationships, and academic achievement (e.g., for reviews, see Krause et al., 2019 and Wickersham et al., 2021). Also, depression during adolescence poses risk for poor psychosocial and health outcomes in adulthood, including lower levels of educational achievement, greater rates of unemployment, and greater risk for adult mental health disorders, and poorer physical health (e.g., Copeland et al., 2021). Moreover, adolescents with depressive symptoms are at higher risk for long-term mental health concerns, including having a three times higher likelihood of adult depression (for a review, see Clayborne et al., 2019). Therefore, it is important to identify factors that may be associated with depressive symptoms during this period to inform intervention efforts (for a review, see Shorey et al., 2022).

At the same time, adolescence is a period of exploration, growth, and adaptation (e.g., Orejudo et al., 2022; Shek et al., 2019). Although initially rooted in deficit-reduction models and focused on understanding adverse outcomes, developmental science no longer conceptualizes adolescence as being marked by “storm and stress” (e.g., Arnett, 1999; Shek et al., 2019). Instead, many scientists now seek to promote and facilitate positive developmental experiences (e.g., building relationships with mentors, engaging in community service) and outcomes (e.g., resilience, social and emotional skills). As one example of this positive youth development (PYD) approach, the 5Cs model highlights adolescent competence, confidence, connection, character, and caring as key characteristics for thriving (Lerner, 2006; Lerner et al., 2011). These five “C”s can be summarized by a single higher-order score of PYD (Geldhof et al., 2014; Jelicic et al., 2007; Lerner, 2006). Importantly, this framework does not place the development of adolescent strengths in opposition to experiences of developmental challenges such as depressive symptoms. Although youth with high PYD often display fewer risk behaviors (Arbeit et al., 2014) and social and emotional difficulties (Shirzad et al., 2024), a central tenet of PYD is that “all youth have strengths” (Agans et al., 2014; Lerner, 2006). It is therefore important to examine the positive development of youth alongside challenges such as depressive symptoms. Furthermore, the PYD framework highlights the interrelations among individuals and contexts that contribute to youth development. Benson (2007) coined the term “developmental assets” to refer to the internal (e.g., motivation, social competencies, identity) and external (e.g., family, school, neighborhood) supports available to PYD. The current study focuses on the family as a key external asset for PYD. External developmental assets, such as a supportive family environment, promote PYD and protect against depressive risk in adolescents (e.g., Belintxon et al., 2020; Orejudo et al., 2022). However, the processes through which parents foster youths’ well-being remain understudied, and family meals offer a concrete context for examining them.

Family Meals

Daily shared family routines and activities, such as family meals, serve as a consistent, predictable ritual that fosters a sense of security, identity, involvement, and belonging within the family unit. The predictable gathering could act as a protective factor, offering emotional security under external stressors (Fiese, Foley, & Spagnola, 2006). Furthermore, family meals may provide children with opportunities for involvement and empowerment through meal preparation, setting the table, and cleaning up afterward (Jones, 2018). This engagement in the process can foster skills, responsibility, and inclusion as valued family members (Jones, 2018).

Family meals have been well-examined as a key factor in promoting children’s development. Systematic reviews identified that family meals are positively associated with fewer eating-related problems, fewer at-risk behaviors, more healthy eating behaviors, improved nutrition outcomes, and better psychosocial outcomes (see Fulkerson et al., 2014 and Harrison et al., 2015). Numerous studies have highlighted the profound benefits of regular family meals for enhancing family connectedness, communication dynamics, and overall well-being (for a review, see Goldfarb et al., 2015). Specifically, youth who have more frequent family meals exhibit stronger parent-child connectedness, better communication skills, higher academic performance, and lower rates of substance abuse, depressive symptoms, and high-risk behaviors (for a review, see Middleton et al., 2020). In sum, family meals have been identified as an important factor for youth development and well-being. However, studies have not examined underlying indirect associations that may explain the relation between family meals and youth outcomes (for a review, see Robson et al., 2020). Family meals are likely to be linked to a more positive family environment, which, in turn, is likely to be associated with better youth well-being. Yet few studies have examined these indirect associations using both youth and parent perspectives.

Family Environment

Family assets (e.g., family environment, shared activities, parent-child relationships) protect against youth depression and support PYD (e.g., Belintxon et al., 2020; Orejudo et al., 2022). Among those family assets, family cohesion and conflict are well-validated factors that are associated with youth well-being (Fosco & Lydon-Staley, 2020). Family cohesion is defined as strong emotional bonds, closeness, support, care, and affection that promote healthy adolescent development (Moos & Moos, 1994). Studies highlight that adolescents from cohesive families are less likely to experience internalizing problems (Deng et al., 2006; Song et al., 2023), and cohesion is associated with higher levels of youth well-being, including higher levels of meaning and purpose in life (Fosco et al., 2012; Fosco & Lydon-Staley, 2020; Lightsey & Sweeney, 2008). In contrast, family conflict, characterized by the presence of anger, hostility, criticism, and tension within the family (Moos & Moos, 1994), is a significant risk factor for the development of both internalizing and externalizing problems (e.g., Lorenzo-Blanco & Unger, 2015; Mowen & Boman, 2018). Hence, family environment—family cohesion and conflict—is linked to youth well-being. Past studies have also specifically found that higher family cohesion mediated the longitudinal association between family meals and lower past-month smoking, lower daily smoking, and lower weekly sweet consumption (Franko et al., 2008; Welsh et al., 2011). However, fewer studies have examined the indirect associations that might explain the relation between family meals and youth psychosocial well-being via family cohesion or conflict. Furthermore, parents and adolescents may have divergent perspectives on family cohesion and conflict, underscoring the need for both family members' perspectives to better understand their roles in relation to youth well-being.

The Current Study

The current study focuses on family meals as a key indicator of shared family routine. Dinner is often the most consistent and predictable shared meal for families with school-aged adolescents and has remained stable across decades of national data (Winship & O’Rourke, 2024; for a review, see Fulkerson et al., 2014). However, while previous research has examined dinner frequency and youth nutritional, physical, and risk outcomes, few have examined the relation between dinner frequency and PYD, and even fewer have examined potential indirect associations such as family environment quality. Importantly, most previous studies on family dinners and family environment have primarily used parent or youth self-reports, which may not capture the shared nature of family routines and experiences. Incorporating multiple informants, such as parent and youth reports for one family, provides a more holistic view of the family processes. Therefore, the current study tested our research questions using the common fate model, which computes latent dyadic-level constructs indicated by parent and youth reports (e.g., Galovan et al., 2017; Ledermann & Macho, 2009; Ledermann & Kenny, 2012). The common fate model uniquely allowed us to simultaneously examine dyadic paths reflecting family-level processes shared by parents and youth, as well as individual paths that represent how family processes contribute to youth development.

Therefore, using the common fate modeling approach, this cross-sectional study examined the relations between family dinner frequency, family cohesion and conflict, youth depressive symptoms and PYD. We first examined the associations between family dinner frequency at the dyad-level (i.e., a latent construct informed by both parent and youth reports) and youth-reported well-being (depressive symptoms and PYD). Then, we investigated whether dyad-level family cohesion and conflict explained the aforementioned associations. Consistent with the prior literature on the advantages of family dinner (e.g., for a review, see Fulkerson et al., 2014), we hypothesized that higher dyad-level family dinner frequency would be associated with lower youth-reported depressive symptoms and higher youth-reported PYD. Further, we hypothesized that more dyad-level frequent family dinners would be associated with higher dyad-level family cohesion and lower family conflict, which, in turn, would be associated with lower youth-reported depressive symptoms and higher youth-reported PYD.

Method

Data were drawn from a study that enrolled participants from the summers of 2020 to 2022, focusing on the recreational and psychosocial health of parents and adolescents (Agans et al., 2024a). The study used an accelerated longitudinal design (Galbraith et al., 2017), but had limited retention across waves, necessitating the use of cross-sectional data only for the current study. We used cross-sectional data from participants who completed our surveys in 2021 or 2022. We retained only those dyads with data from both a parent and a youth and used the first available response for participants with multiple waves of data.

Participants

Participants (Ndyads = 166)1 completed an online REDCap survey in the summer of 2021 or 2022. Among parents (Mage = 41.2, SD = 4.83), 36% self-identified as men, 63% as women, and 1% as nonbinary. Youth (Mage = 15.2, SD = 1.35) in the current study were 51% boys, 47% girls, and 2% nonbinary or transgender. Regarding race/ethnicity, 54.2% of the youth self-identified as White, 25.3% as Hispanic or Latino/Latina, 6% as Black or African American, 4.8% as Asian or Asian American, 2.4% as American Indian or Alaska Native, and 7.2% as multiracial. The majority of parents identified as White (56.4%), then as Hispanic or Latino (26.7%), Black or African American (6.1%), Asian or Asian American (3.6%), American Indian or Alaska Native (2.4%), and Multiracial (4.8%). Over half of the participants (54.5%) lived in households where parents reported an annual household income over $75,000, 30.3% earned $50,000 to $74,000 per year, and 15.1% earned less than $50,000 per year. Regarding parental education and employment, 51.2% of parents had a bachelor’s degree or higher, and 79.5% were employed and worked when they completed the survey.

Procedures

Recruitment was conducted via snowball sampling and social media in the summers of 2020, 2021, and 2022. Each year, research staff contacted educators and leaders of youth-serving organizations in their professional networks to disseminate information about the research sampling. Educators and leaders resided in 13 states, including CA, CT, IL, ME, MN, MO, NH, NJ, NM, NY, PA, VT, and WA. Also, paid advertisements were used on Twitter and Facebook for 2 months each year. To be eligible for the study, participants had to reside in the U.S. and be between the ages of 13–18 in 2021 or 14–19 in 2022. Interested parents first provided consent and permission for their child under the age of 18 to participate. They then completed a screener that assessed U.S. residency, youth age, and biological parent or legal guardian status. The screener also included attention-check items and collected contact information, including the parent’s name and email address, as well as the youth’s email address. A research assistant reviewed this information to exclude untrustworthy or suspicious entries (Agans et al., 2024a) and sent links to the full survey to eligible parent and youth participants within 72 hours. Youth participants provided assent (or consent if they were 18 or older) and confirmed their eligibility. Each respondent received a $10 gift card upon completing the survey (approximately twenty-five minutes). All procedures discussed above in this study were approved by the Pennsylvania State University Institutional Review Board (STUDY00017666). More study information can be found in previously published studies (e.g., Agans et al., 2024b; Shirzad et al., 2024). This study was not preregistered. Measures and analytic procedures are described below. Reasonable requests for data and study materials can be made to the last author.

Measures

Demographic Variables

We included parent age, youth age, parent gender, youth gender, parent race/ethnicity, and family income as covariates as they were significantly associated with primary study variables and have been highlighted in prior studies (e.g., Jackson & Goodman, 2011; Marín-Gutiérrez & Caqueo-Urízar, 2025). Due to the small percentage of participants (parent = 1%; youth = 2%) who identified as a gender minority in the dataset, we only retained participants who identified as men (0) and women (1). Race/ethnicity of parent was recategorized into two groups: 0 = White, and 1 = people of color, as about 55% of participants identified themselves as White. The next two largest groups represented in the people of color category of participants were Hispanic or Latino (26% of the total sample) and Black or African American (6% of the total sample)2. Parents reported the previous year’s family income on an 8-point scale ranging from $14,999 or less to $200,000 or higher2.

Predictor Variables

Family Dinner Frequency.

Family dinner frequency was measured by asking parents and youth to rate how frequently they had family dinners together. They answered the following question separately: “Currently, how often did you eat dinner with your family on a typical week?” on a 4-point scale that ranged from 0 (Never) to 4 (Always). Interrater reliability for parent and youth-reported family dinner frequency was high (ICC = .84). Considering the high correlation between parent and youth-reported (r = .72, p < 0.001), family dinner frequency was modeled at the dyad level using a common fate modeling approach (Ledermann & Kenny, 2012).

Family environment.

Parents and youth reported on their family environment individually using the Family Environment Scale (10 items; Moos & Moos, 1994). Items were rated on a 4-point scale: Very untrue for my family (1), Fairly untrue for my family (2), Fairly True for my family (3), and Very True for my family (4). Five items referred to family cohesion (e.g., “family members really help and support one another”), and another five referred to family conflict (e.g., “we fight a lot in our family”). Internal consistency for cohesion was acceptable (parent Cronbach’s α =.74; youth Cronbach’s α =.76). However, the initial five-item conflict subscale showed relatively low internal consistency for both parents and youth reports (αs = .60–.55). Therefore, we conducted a confirmatory factor analysis (CFA) to evaluate the hypothesized five-item factor structure. For both parent- and youth-reported items, the two reverse-coded items, Item 3, “Family members hardly ever lost their tempers,” and Item 5, “Family members rarely criticized each other”, had lower factor loadings (λs = .139–.174 and .072–.104, respectively) than the other three items. These results suggested that in our sample, the reverse-coded items may not align well with the primary conflict construct assessed by the remaining items; thus, we removed them. The remaining three-item conflict subscales demonstrated acceptable reliability for both parent and youth reports (αs = .64–.73).

All items were averaged to create two subscales. Higher total scores indicated higher cohesion or higher conflict in the family environment. Interrater reliability for parent and youth-reported family cohesion and conflict, as measured by the intraclass correlation coefficient (ICC), was .72 and .75, respectively. Given the moderate correlations between parent and youth-reported family cohesion (r = .57, p < .001) and conflict (r = .61, p < .001), both were modeled at the dyad level using a common fate modeling approach (Ledermann & Kenny, 2012). Consistent with the imputation cut-offs used for the other scales (missingness < 20%; Radloff, 1977), two parents who did not answer sufficient items (full 3 items) on family conflict, and four youth who did not answer sufficient items (>1 out of 5 items or full 3 items) on both family cohesion and conflict were excluded and coded as missing. Item-mean imputation was used for five parents and four youth with less than 20% (≤1 out of 5 items) missingness on family cohesion.

Outcome Variables

Depressive Symptoms.

Youth rated their depressive symptoms using the Center for Epidemiologic Studies Depression Scale (CESD; 20 items; Radloff, 1977). Items include statements like “I was bothered by things that don't usually bother me,” and “I felt fearful.” Four item responses were reverse scored (“I felt that I was just as good as other people”, “I felt hopeful about the future”, “I was happy”, and “I enjoyed life”). Youth participants completed Likert scales that ranged from Rarely or none of the time (less than 1 day; 0) to Most or all of the time (5–7 days; 3). Items were summed to create overall scores; higher total scores indicate more depressive symptoms (Cronbach’s α = .91). Consistent with prior publications (Radloff, 1977), we excluded two participants’ depressive symptom scores, which were coded as missing because more than four items were missing (> 20%). For the eight youth who missed fewer than four items, we used item-mean imputation to replace missing items and then summed the total CESD scores (e.g., Bono et al., 2007).

Positive Youth Development.

Youth rated their positive attributes using a 17-item short version measure assessing their positive development (PYD; Geldhof et al., 2014). This scale assesses overall PYD, as well as the five “C’s” of PYD: competence, confidence, connection, character, and caring. Youth participants used Likert scales from 1 (Strongly Disagree) to 5 (Strongly Agree) to indicate their level of agreement with items (e.g., “I have a lot of friends),” how important items were to their lives (e.g., “Helping to make the world a better place to live in”) and the ability of certain statements to describe them (e.g., “I enjoy being with people who are of a different race than I am”). Items were averaged to create an overall PYD score (Cronbach’s α = .88). Prior research on the psychometric properties of PYD scales indicate that a higher-order PYD factor effectively represents the five lower-order constructs (Geldhof et al., 2014; Jelicic et al., 2007; Lerner, 2006). Item-mean imputation was used for three youth missing responses to three or fewer items (< 20%).

Plan of Analyses

We examined descriptive statistics and bivariate correlations among key study variables and demographic variables of interest in SPSS V.29 (IBM Corp., 2023). We included parent age, youth age, parent gender, youth gender, parent race/ethnicity, and family income as covariates, modeling their associations with the primary outcomes. All hypotheses were tested using the common fate model in the structural equation modeling (SEM) approach with the lavaan package (0.6–19) in R 4.4.1 (R Core Team, 2024), to precisely account for individual- and dyad-level processes (Galovan et al., 2017; Ledermann & Macho, 2009). For each model, we used the standard fit indices to assess model fit: root mean square error of approximation (RMSEA) below .08; Comparative Fit Index (CFI) above .90; and SRMR below .10.

Common fate models are ideal when family members report on a construct that can be described as dyad level, as often indicated by questions that include terms such as “our family” or “we”. In the common fate models, the outcome variables, depressive symptoms and PYD were individual-level constructs that were reported by the youth only. Predictor variables, family dinner frequency, cohesion, and conflict that both parents and youth reported were constructed as dyad-level latent variables (i.e., common fate constructs). For example, two individual-level variables, youth report of family dinner frequency and parent report of family dinner frequency, were designated as indicators of a latent variable, family dinner frequency, at the dyad level. The residual covariance between the two indicators was fixed to zero. These latent dyad-level variables are intended to represent the dyad’s perception of a shared event (e.g., dinner) or experience (e.g., cohesion), assuming interdependence between parent and youth reports (Galovan et al., 2017; Ledermann & Kenny, 2012).

To examine our first hypothesis, we applied the common fate model to conduct linear regression models examining the association between family dinner frequency (a latent dyadic variable) and youth depressive symptoms and PYD, in two separate models. To examine our second hypothesis, we applied the common fate model to conduct four hybrid models. The hybrid models examined two primary paths. First, correlations between latent variables at the dyad-level family dinner frequency and family cohesion or conflict were modeled. Second, we examined the indirect associations of family dinner frequency (the latent variable) on youth depressive symptoms and PYD (two measured variables) via family cohesion or conflict (the latent variable). These indirect associations, computed as the product of paths a and b (indirect = a*b), reflected how family dinners were linked to individual-level youth outcomes through the quality of the family environment (Ledermann & Macho, 2009). As with the first hypothesis, the residual covariance between the two indicators was fixed to zero. However, consistent with the common fate model, we modeled covariances between the error terms of parent report of family dinner frequency and parent report of the mediators (e.g., family cohesion), as well as covariances between the error terms of parallel youth reports. The hybrid model approach allowed us to capture the dyad-level processes that are simultaneously associated with individual development within the broader family system. We tested indirect pathways using 5,000 bootstrap samples to estimate standard errors, avoiding bias-corrected significance levels (Shrout & Bolger, 2002). Indirect associations were evaluated using bootstrapped 95% confidence intervals (CIs); results were considered significant if the CI did not include zero. To assess model fit, the two-factor loadings were set to 1 for each common fate variable (family dinner frequency, family cohesion, and family conflict).

Results of the missing data analysis conducted prior to variable scoring using Little’s Missing Completely at Random (MCAR) test, based on the 59 scale items of eight primary study variables and 166 data points each, indicated that the data in the analytic sample were missing completely at random, χ2(1251) = 1252.770, p = .481. Missing data for scale items ranged from 0.6% to 3.6%, with youth-reported variables (dinner frequency, family cohesion, and conflict) missing most frequently. Thus, item mean imputation was used during variable scoring when participants missed fewer than 20% of the items, consistent with the cut-offs used for the other scales (Radloff, 1977). In the main analyses, missing data were handled using full information maximum likelihood (FIML), which uses all available information to ensure unbiased estimation of parameters (Enders, 2010).

Results

Preliminary Analyses

Descriptive statistics and correlations are in Table 1. Parent-reported family dinner frequency was positively associated with parent-reported (r = .35, p < .001) and youth-reported (r = .33, p < .001) family cohesion, youth-reported family dinner frequency (r = .72, p < .001), and youth-reported PYD (r = .40, p < .001); parent-reported family dinner frequency was negatively associated with parent-reported (r = −.29, p = .001) and youth-reported (r = −.27, p < .001) family conflict and youth-reported depressive symptoms (r = −.32, p < .001).

Table 1.

Descriptive Statistics and Bivariate Correlations Among Study Variables (N = 166; Only Continuous Variables).

Variable 1 2 3 4 5 6 7 8 9 10
Parent Report
1. Dinner frequency —
2. Family cohesion .35*** —
3. Family conflict −.29*** −.63*** —
Youth Report
4. Dinner frequency .72*** .26*** −.26** —
5. Family cohesion .33*** .57*** −.52*** .37*** —
6. Family conflict −.27** −.45*** .61*** −.20** −.64*** —
7. Depressive symptoms −.32*** −.36*** .49*** −.27*** −.57*** .58*** —
8. PYD .40*** .46*** −.48*** .40*** .59*** −.60*** −.63*** —
Demographic
9. Parent age −.22** .03 −.04 −.16* −.15 .16* .20* −.11 —
10. Youth age −.20* −.18* .13 −.25** −.19* .14 .20* −.18* .35*** —
Mean 3.34 3.25 1.42 3.42 3.18 1.45 13.54 3.94 41.19 15.17
Standard Deviation 0.79 0.51 0.52 0.83 0.57 0.55 8.97 0.55 4.83 1.35
Open in a new tab

Note.

*

p < .05;

**

p < .01;

***

p < .001.

PYD = Positive Youth Development.

Primary Analyses

Tests of Associations Between Family Dinner Frequency and Youth Outcomes

To address the first research question, we used two common fate regression models to examine the main associations of dyad-level family dinner frequency on youth depressive symptoms and PYD. The measurement model demonstrated parents’ and youth’s reported dinner frequency adequately loaded onto a dyad-level dinner frequency latent factor (for the youth depressive symptom model, λ = 0.88 and 0.82, respectively; for the PYD model, λ = 0.87 and 0.83, respectively). After accounting for covariates, family dinner frequency, a latent dyad-level construct, was significantly negatively associated with youth depressive symptoms (R2 = .186, β = −.33, p < .001), explaining 8.7% of unique variance beyond covariates, and significantly positively associated with PYD (R2 = .346, β = .50, p < .001), explaining 23.9% of the unique variance beyond covariates.

Tests of Indirect Associations (see Figures 2 and 3)

Figure 2. Standardized Hybrid Models for Youth Depressive Symptoms.

Figure 2.

Open in a new tab

Note. CI = confidence interval. *p < .05; **p < .01; ***p < .001

Figure 3. Standardized Hybrid Models for Positive Youth Development.

Figure 3.

Open in a new tab

Note. CI = confidence interval. *p < .05; **p < .01; ***p < .001

Model 1. The Association between Dyad-level Family Dinner Frequency with Youth Depressive Symptoms Explained by Dyad-level Family Cohesion.

The model fit the data acceptable: χ2(19) = 32.405, p = .028; CFI = .947; RMSEA = .065; SRMA = .043. More dyad-level frequent family dinner was significantly associated with higher family cohesion (path a; β = .48, p < .001). In turn, family cohesion was negatively associated with youth depressive symptoms (path b; β = −.64, p < .001). There was a significant negative indirect association of family dinner frequency with depressive symptoms, explained by family cohesion (β = −.23, 95% CI [−5.206, −1.276]). The direct association between family dinner frequency and youth depressive symptoms was non-significant (path c′; β = −.04, p = .682). This model explained 37.6% of the unique variance in youth depressive symptoms beyond covariates.

Model 2. The Association between Dyad-level Family Dinner Frequency with Youth Depressive Symptoms Explained by Dyad-level Family Conflict3.

The model fit the data well: χ2(22) = 23.819, p = .357; CFI = .993; RMSEA = .022; SRMA = .039. More dyad-level frequent family dinner was significantly associated with lower family conflict (path a; β = −.45, p < .001). In turn, family conflict was positively associated with youth depressive symptoms (path b; β = .70, p < .001). There was a significant negative indirect association of family dinner frequency with youth depressive symptoms, explained by family conflict (β = −.31, 95% CI [−8.710, −1.614]). The direct association between family dinner frequency and youth depressive symptoms was non-significant (path c′; β = −.04, p = .714). This model explained 46.2% of the unique variance in youth depressive symptoms beyond covariates.

Model 3. The Association of Dyad-level Family Dinner Frequency with Positive Youth Development Explained by Dyad-level Family Cohesion.

The model fit the data acceptable: χ2(20) = 30.144, p = .068; CFI = .963; RMSEA = .055; SRMA = .045. More dyad-level frequent family dinner was significantly associated with higher family cohesion (path a; β = .48, p < .001). In turn, family cohesion was significantly positively associated with PYD (path b; β = .61, p < .001). There was a significant positive indirect association of family dinner frequency with PYD, explained by family cohesion (β = .25, 95% CI [0.096, 0.326]). The direct association between family dinner frequency and PYD was significant (path c′; β = .22, p = .013). This model explained 49.8% of the unique variance in PYD beyond covariates.

Model 4. The Association of Dyad-level Family Dinner Frequency with Positive Youth Development Explained by Dyad-level Family Conflict4.

The model fit the data well: χ2(22) = 26.859, p = .217; CFI = .982; RMSEA = .036; SRMA = .042. More dyad-level frequent family dinner was associated with lower family conflict (path a; β = −.45, p < .001). In turn, family conflict was significantly negatively associated with PYD (path b; β = −.57, p < .001). There was a significant positive indirect association of family dinner frequency with PYD, explained by family conflict (β = .27, 95% CI [0.085, 0.501]). The direct association between family dinner frequency and PYD was significant (path c′; β = .28, p = .002). This model explained 48.5% of the unique variance in PYD beyond covariates.

Discussion

Family meals serve as a unique and structured opportunity for family members to interact, connect, and communicate with each other, as well as a space to foster emotional bonds within the family. The current study examined associations between family dinner frequency, family environment quality, and youth well-being (depressive symptoms and PYD) using the common fate model, which integrates multi-informant perspectives to capture shared family processes and increase our understanding of family influences on youth development (Galovan et al., 2015; Ledermann & Macho, 2009). The use of the common fate model marks an important methodological advancement, as previous studies have mostly relied on a single informant (self-report), which may be less able to capture the interactive nature of family contexts. By incorporating reports from more than one family member, the common fate model captures a shared perception of family experiences and environment, allowing for a more holistic, parsimonious, and less biased understanding of family process. This approach enhances the rigor of the findings and affirms prior research linking family dinner and family environment to youth well-being by demonstrating that these associations hold when family dinner frequency and family environment are treated as a shared family experience.

Consistent with our hypothesis and prior literature (e.g., Goldfarb et al., 2015; for reviews, see Harrison et al., 2015, and Fulkerson et al., 2014), family dinner frequency was negatively associated with youth depressive symptoms and positively associated with PYD. Family cohesion and conflict explained the associations between family dinner frequency with both depressive symptoms and PYD, supporting the notion that family dinners are positively associated with youth well-being because they provide increased opportunity for connection and understanding among family members. Specifically, family cohesion, characterized by emotional closeness, support, and care (Moos & Moos, 1994), explained the association between family dinner frequency and indicators of youth well-being. Prior studies have documented that cohesive family environments are linked to better youth well-being and less youth mental health risks (e.g., Deng et al., 2006; Fosco et al., 2012). Past studies have also found that family dinners can provide structured opportunities for open communication, emotional bonding, and collective problem-solving, which contribute to a positive family environment (e.g., Goldfarb et al., 2015). For example, engagement in meaningful conversations or warm check-ins during meals, it may strengthen emotional bonds (family cohesion) and help clarify or resolve misunderstandings (family conflict). Consistent with past research, we found that families who eat dinner together more often also report higher levels of family cohesion; in turn, higher levels of family cohesion were associated with youth lower levels of depressive symptoms and higher levels of PYD.

Family conflict, characterized by hostility, criticism, and tension within the family (Moos & Moos, 1994), also played a role in explaining the associations between family dinner frequency and youth well-being. Higher family dinner frequency was associated with less family conflict, which in turn was associated with lower youth depressive symptoms and higher PYD. Findings highlight the potential dual roles of family dinners, which may be linked to more cohesive family interactions or to proactive prevention of negative interactions or tensions. Importantly, these findings expand on previous research that primarily focused on family cohesion as a mechanism but not on family conflict. This underscores the need to capture both positive and negative aspects of family functioning to better support youth development.

Interestingly, direct associations between family dinner frequency and youth well-being were significant for PYD models but non-significant for depressive symptoms, suggesting that the associations of family dinner on youth depressive symptoms are largely indirect, primarily operating through family cohesion and conflict. Family dinner represents only one of many strategies to promote family cohesion and reduce family conflict, and not all families have the flexibility or resources to share regular dinners together. Possible barriers include youth or parents’ schedule conflicts, shift-based or long-distance employment, or other socioeconomic constraints (e.g., Jones et al.,2023; Neumark-Sztainer et al., 2003). Our indirect association findings demonstrate that regardless of what families are doing together, establishing family routines linked with better family cohesion and less family conflict is likely the crucial factor associated with better youth well-being. For families with limited time together, intentionally using available moments, such as breakfasts, after-school activities, weekend check-ins, or bedtime rituals, to foster emotional connection and reduce conflict may provide similar benefits to youth well-being (Selman & Dilworth-Bart, 2024; Spagnola & Fiese, 2007; Walsh, 2012).

Limitations and Future Research

Although the current study has several important strengths, such as the use of the common fate model to capture perspectives from multiple family members and the consideration of potential indirect associations to explain the relations between family dinner, youth depressive symptoms, and PYD, some limitations should be noted. First, the current study used a cross-sectional design, using data from a single time point per dyad. Thus, although the indirect paths provide useful exploratory information, the results should not be interpreted as evidence of true mediation or ascertain directionality (Georgeson et al., 2025; Maxwell & Cole, 2007). For example, it is possible that youth with higher depressive symptoms experience less family cohesion or more family conflict, which in turn reduces family dinner frequency. Alternatively, a more cohesive family may have more family dinners, which in turn leads to better youth well-being. Thus, the results of indirect associations should be interpreted as exploratory but not causal between family dinner and family environment. Future research should consider longitudinal designs that would allow for a more robust examination of directionality. Second, this study focused only on family dinners, which limits its scope. Other meals and shared family routines also play important roles in shaping the emotional climate of the family and youth well-being. Additionally, weekend activities or special family gatherings may provide additional opportunities for connection and communication. Expanding future studies to include a comprehensive assessment of different family activities and rituals would provide a broader understanding of protective family processes in diverse populations. Third, the current study did not explore differences by race, ethnicity, or culture, as it lacked sufficient power to examine group differences. Cultural norms surrounding mealtime practices, family structure (e.g., single-parent households), or socioeconomic factors (e.g., income, employment) may be associated with how family activities and their impacts on the family environment and youth well-being. For example, mealtime rituals in collectivist cultures may differ significantly from those in individualistic cultures, potentially resulting in different effects (e.g., Ochs & Shohet, 2006). Future mixed-methods studies can provide an in-depth understanding of what routine family activities, such as family dinners, mean for families and how they relate to youth well-being. Lastly, the study used an online convenience sample, which may introduce self-selection bias (e.g., families with reliable internet access and comfort with online surveys) and limit the generalizability of the findings to the broader population (Bethlehem, 2010). Further studies could consider more representative sampling approaches to better address the needs of the populations they serve (e.g., school or community-based recruitment).

Conclusion

Family meals are generally considered a beneficial routine for youth well-being. Yet, family dinners vary in frequency and structure across households, and the mechanisms through which they relate to youth depressive symptoms and PYD remain understudied. While prior reviews have documented the associations between family dinner and youth development, including better nutrition and physical outcomes, less risk behavior, and better mental health (see Fulkerson et al., 2014, Harrison et al., 2015, and Middleton et al., 2020), our study further clarified the relations between family dinner frequency and youth depressive symptoms and PYD by examining indirect associations via family cohesion and conflict. Notably, our study applied the common fate model approach to integrate youth and parent perspectives, offering a more holistic and less biased view of how shared routines are associated with the family processes. By highlighting the potential dual role of family meals with family processes, our findings suggest that it is not only the frequency of family dinners that matters, but the quality of family interactions during these moments. These findings suggest that prevention efforts aimed at supporting youth well-being may benefit from encouraging routine family activities, with an emphasis on positive family interactions during them. Future research should explore these pathways using longitudinal designs and in more diverse families to better understand how family routines can foster supportive environments for youth well-being.

Funding:

This work was supported by The Pennsylvania State University College of Health and Human Development. The study was also supported by NIH/NCATS Grant #UL1TR000127 and UL1TR002014 in its use of the REDCap survey platform.

The authors sincerely thank the study participants. This study was not preregistered. Data are not publicly available, but reasonable requests for data and study materials can be made to the last author. Analytic methods are described in the article, and further details (e.g., codes) are available upon request to the corresponding author. The authors report all data exclusions, manipulations, and measures in the study. Some of the data and ideas in the article were previously presented at the Society for Research on Adolescence Biennial Meeting 2023.

Footnotes

1

Of the 166 participants, 141 (85%) completed the survey in 2021, and 25 (15%) completed the survey in 2022. Of the 141 dyads who completed the survey in 2021, 17 (12%) were returning participants who returned after first enrolling in 2020, and 124 (88%) were newly recruited. Of the 25 dyads who completed the survey in 2022, 8 (32%) provided complete data for the first time in 2022 although they enrolled in 2021, and 17 (68%) were newly recruited.

2

Family Income options were: $14,999 or less; $15,000 to $29,000; $30,000 to $49,000; $50,000 to $74,000; $75,000 to $99,000; $100,000 to $149,000; $150,000 to $199,000; and $200,000 or more.

3

An exploratory parallel multiple mediation model that simultaneously tested indirect associations via dyad-level family cohesion and conflict resulted in poor model fit for youth depressive symptoms: χ2(46) = 144.377, p < .001; CFI = .771; RMSEA = .114; SRMA = .081.

4

An exploratory parallel multiple mediation model that simultaneously tested indirect associations via dyad-level family cohesion and conflict resulted in poor model fit for positive youth development: χ2(46) = 161.495, p < .001; CFI = .750; RMSEA = .123; SRMA = .090.

References

  1. Agans JP, Champine RB, DeSouza LM, Mueller MK, Johnson SK, & Lerner RM (2014). Activity involvement as an ecological asset: Profiles of participation and youth outcomes. Journal of Youth and Adolescence, 43, 919–932. 10.1007/s10964-014-0091-2 [DOI] [PubMed] [Google Scholar]
  2. Agans JP, Schade SA, Hanna SR, Chiang S-C, Shirzad K, & Bai S (2024a). The inaccuracy of data from online surveys: A cautionary analysis. Quality & Quantity, 58(3), 2065–2086. 10.1007/s11135-023-01733-5 [DOI] [Google Scholar]
  3. Agans JP, Hanna S, Reed KB, Stillwell C, & Bai S (2024b). Leisure and psychosocial functioning during the COVID-19 pandemic: Exploring data from adolescents and parents of adolescents. Journal of Leisure Research, 55(4), 567–587. 10.1080/00222216.2024.2315050 [DOI] [Google Scholar]
  4. Arbeit MR, Johnson SK, Champine RB, Greenman KN, Lerner JV, & Lerner RM (2014). Profiles of problematic behaviors across adolescence: Covariations with indicators of positive youth development. Journal of Youth and Adolescence, 43, 971–990. 10.1007/s10964-014-0095-y [DOI] [PubMed] [Google Scholar]
  5. Arnett JJ (1999). Adolescent storm and stress, reconsidered. American Psychologist, 54(5), 317–326. 10.1037/0003-066X.54.5.317 [DOI] [PubMed] [Google Scholar]
  6. Belintxon M, Osorio A, De Irala J, Van Riper M, Reparaz C, & Vidaurreta M (2020). Connections between family assets and positive youth development: The association between parental monitoring and affection with leisure-time activities and substance use. International Journal of Environmental Research and Public Health, 17(21), 8170. 10.3390/ijerph17218170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Benson PL (2007). Developmental assets: An overview of theory, research, and practice. In Silbereisen RK & Lerner RM (Eds.), Approaches to positive youth development (pp. 33–58). SAGE Publications. [Google Scholar]
  8. Bethlehem J (2010). Selection bias in web surveys. International Statistical Review, 78(2), 161–188. 10.1111/j.1751-5823.2010.00112.x [DOI] [Google Scholar]
  9. Bono C, Ried LD, Kimberlin C, & Vogel B (2007). Missing data on the Center for Epidemiologic Studies Depression Scale: A comparison of 4 imputation techniques. Research in Social and Administrative Pharmacy, 3, 1–27. 10.1016/j.sapharm.2006.04.001 [DOI] [PubMed] [Google Scholar]
  10. Clayborne ZM, Varin M, & Colman I (2019). Systematic review and meta-analysis: Adolescent depression and long-term psychosocial outcomes. Journal of the American Academy of Child & Adolescent Psychiatry, 58(1), 72–79. 10.1016/j.jaac.2018.07.896 [DOI] [PubMed] [Google Scholar]
  11. Copeland WE, Alaie I, Jonsson U, & Shanahan L (2021). Associations of childhood and adolescent depression with adult psychiatric and functional outcomes. Journal of the American Academy of Child & Adolescent Psychiatry, 60(5), 604–611. 10.1016/j.jaac.2020.07.895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Deng S, Lopez V, Roosa MW, Ryu E, Burrell GL, Tein J-Y, & Crowder S (2006). Family processes mediating the relationship of neighborhood disadvantage to early adolescent internalizing problems. The Journal of Early Adolescence, 26(2), 206–231. 10.1177/0272431605285720 [DOI] [Google Scholar]
  13. Enders CK (2010). Applied missing data analysis. New York, NY: The Guilford Press. [Google Scholar]
  14. Fiese BH, Foley KP, & Spagnola M (2006). Routine and ritual elements in family mealtimes: Contexts for child well-being and family identity. New Directions for Child and Adolescent Development, 2006(111), 67–89. 10.1002/cd.156 [DOI] [PubMed] [Google Scholar]
  15. Fosco GM, Caruthers AS, & Dishion TJ (2012). A six-year predictive test of adolescent family relationship quality and effortful control pathways to emerging adult social and emotional health. Journal of Family Psychology, 26(4), 565–575. 10.1037/a0028873 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fosco GM, & Lydon-Staley DM (2020). Implications of family cohesion and conflict for adolescent mood and well-being: Examining within- and between-family processes on a daily timescale. Family Process, 59(4), 1672–1689. 10.1111/famp.12515 [DOI] [PubMed] [Google Scholar]
  17. Franko DL, Thompson D, Affenito SG, Barton BA, & Striegel-Moore RH (2008). What mediates the relationship between family meals and adolescent health issues. Health Psychology, 27(2, Suppl), S109–S117. 10.1037/0278-6133.27.2(Suppl.).S109 [DOI] [PubMed] [Google Scholar]
  18. Fulkerson JA, Larson N, Horning M, & Neumark-Sztainer D (2014). A review of associations between family or shared meal frequency and dietary and weight status outcomes across the lifespan. Journal of Nutrition Education and Behavior, 46(1), 2–19. 10.1016/j.jneb.2013.07.012 [DOI] [PubMed] [Google Scholar]
  19. Galbraith S, Bowden J, & Mander A (2017). Accelerated longitudinal designs: An overview of modelling, power, costs and handling missing data. Statistical Methods in Medical Research, 26(1), 374–398. 10.1177/0962280214547150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Galovan AM, Holmes EK, & Proulx CM (2017). Theoretical and methodological issues in relationship research: Considering the common fate model. Journal of Social and Personal Relationships, 34(1), 44–68. 10.1177/0265407515621179 [DOI] [Google Scholar]
  21. Geldhof GJ, Bowers EP, Mueller MK, Napolitano CM, Callina KS, & Lerner RM (2014). Longitudinal analysis of a very short measure of positive youth development. Journal of Youth and Adolescence, 43(6), 933–949. 10.1007/s10964-014-0093-z [DOI] [PubMed] [Google Scholar]
  22. Georgeson AR, Alvarez-Bartolo D, & MacKinnon DP (2025). A sensitivity analysis for temporal bias in cross-sectional mediation. Psychological Methods, 30(6), 1326–1344. 10.1037/met0000628 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Goldfarb SS, Tarver WL, Locher JL, Preskitt J, & Sen B (2015). A systematic review of the association between family meals and adolescent risk outcomes. Journal of Adolescence, 44(1), 134–149. 10.1016/j.adolescence.2015.07.008 [DOI] [PubMed] [Google Scholar]
  24. Harrison ME, Norris ML, Obeid N, Fu M, Weinstangel H, & Sampson M (2015). Systematic review of the effects of family meal frequency on psychosocial outcomes in youth. Canadian Family Physician, 61(2), e96–e106. [PMC free article] [PubMed] [Google Scholar]
  25. Jackson B, & Goodman E (2011). Low Social Status Markers: Do They Predict Depressive Symptoms in Adolescence?. Race and Social Problems, 3(2), 119–128. 10.1007/s12552-011-9047-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jelicic H, Bobek DL, Phelps E, Lerner RM, & Lerner JV (2007). Using positive youth development to predict contribution and risk behaviors in early adolescence: Findings from the first two waves of the 4-H Study of Positive Youth Development. International Journal of Behavioral Development, 31(3), 263–273. 10.1177/0165025407076439 [DOI] [Google Scholar]
  27. Jones BL (2018). Making time for family meals: Parental influences, home eating environments, barriers and protective factors. Physiology & Behavior, 193, 248–251. 10.1016/j.physbeh.2018.03.035 [DOI] [PubMed] [Google Scholar]
  28. Jones BL, Orton AL, Tindall SW, Christensen JT, Enosakhare O, Russell KA, & Reynolds C (2023). Barriers to healthy family dinners and preventing child obesity: Focus group discussions with parents of 5-to-8-year-old children. Children, 10(6), 952. 10.3390/children10060952 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Krause KR, Bear HA, Edbrooke-Childs J, & Wolpert M (2019). What outcomes count? Outcomes measured for adolescent depression between 2007 and 2017. Journal of the American Academy of Child & Adolescent Psychiatry, 58(1), 61–71. 10.1016/j.jaac.2018.07.893 [DOI] [PubMed] [Google Scholar]
  30. Ledermann T, & Macho S (2009). Mediation in dyadic data at the level of the dyads: A Structural Equation Modeling approach. Journal of Family Psychology, 23(5), 661. 10.1037/a0016197 [DOI] [PubMed] [Google Scholar]
  31. Ledermann T, & Kenny DA (2012). The common fate model for dyadic data: Variations of a theoretically important but underutilized model. Journal of Family Psychology, 26(1), 140–148. 10.1037/a0026624 [DOI] [PubMed] [Google Scholar]
  32. Lerner RM (2006). Developmental science, developmental systems, and contemporary theories of human development. In Damon W & Lerner RM (Eds.), Handbook of child psychology (6th ed., Vol. 1, pp. 1–17). Wiley. [Google Scholar]
  33. Lerner RM, Lerner JV, & Benson JB (2011). Positive youth development. In Advances in Child Development and Behavior (Vol. 41, pp. 1–17). Elsevier. 10.1016/B978-0-12-386492-5.00001-4 [DOI] [PubMed] [Google Scholar]
  34. Lightsey OR, & Sweeney J (2008). Meaning in life, emotion-oriented coping, generalized self-efficacy, and family cohesion as predictors of family satisfaction among mothers of children with disabilities. The Family Journal, 16(3), 212–221. 10.1177/1066480708317503 [DOI] [Google Scholar]
  35. Lorenzo-Blanco EI, & Unger JB (2015). Ethnic Discrimination, acculturative stress, and family conflict as predictors of depressive symptoms and cigarette smoking among latina/o youth: The mediating role of perceived stress. Journal of Youth and Adolescence, 44(10), 1984–1997. 10.1007/s10964-015-0339-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lu B, Lin L, & Su X (2024). Global burden of depression or depressive symptoms in children and adolescents: A systematic review and meta-analysis. Journal of Affective Disorders, 345, 156–168. 10.1016/j.jad.2024.01.045 [DOI] [PubMed] [Google Scholar]
  37. Marín-Gutiérrez M, & Caqueo-Urízar A (2025). The influence of social determinants and 5Cs of positive youth development on the mental health of Chilean adolescents. BMC Psychology, 13(1), 333. 10.1186/s40359-025-02629-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Maxwell SE, & Cole DA (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23. 10.1037/1082-989X.12.1.23 [DOI] [PubMed] [Google Scholar]
  39. McLaughlin KA, & King K (2015). Developmental trajectories of anxiety and depression in early adolescence. Journal of Abnormal Child Psychology, 43, 311–323. 10.1007/s10802-014-9898-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Middleton G, Golley R, Patterson K, Le Moal F, & Coveney J (2020). What can families gain from the family meal? A mixed-papers systematic review. Appetite, 153, 104725. 10.1016/j.appet.2020.104725 [DOI] [PubMed] [Google Scholar]
  41. Moos RH, & Moos BS (1994). Family environment scale manual. Palo Alto, CA: Consulting Psychologists Press. [Google Scholar]
  42. Mowen TJ, & Boman JH (2018). A developmental perspective on reentry: Understanding the causes and consequences of family conflict and peer delinquency during adolescence and emerging adulthood. Journal of Youth and Adolescence, 47(2), 275–289. 10.1007/s10964-017-0794-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. National Institute of Mental Health. (n.d.). Major depression. U.S. Department of Health and Human Services, National Institutes of Health. Retrieved April 17, 2025, from https://www.nimh.nih.gov/health/statistics/major-depression [Google Scholar]
  44. Neumark-Sztainer D, Hannan PJ, Story M, Croll J, & Perry C (2003). Family meal patterns: Associations with sociodemographic characteristics and improved dietary intake among adolescents. Journal of the American Dietetic Association, 103(3), 317–322. 10.1053/jada.2003.50048 [DOI] [PubMed] [Google Scholar]
  45. Ochs E, & Shohet M (2006). The cultural structuring of mealtime socialization. New Directions for Child and Adolescent Development, 2006(111), 35–49. 10.1002/cd.155 [DOI] [PubMed] [Google Scholar]
  46. Orejudo S, Balaguer Á, Osorio A, De La Rosa PA, & Lopez-del Burgo C (2022). Activities and relationships with parents as key ecological assets that encourage personal positive youth development. Journal of Community Psychology, 50(2), 896–915. 10.1002/jcop.22689 [DOI] [PubMed] [Google Scholar]
  47. Radloff LS (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. 10.1177/014662167700100306 [DOI] [Google Scholar]
  48. Robson SM, McCullough MB, Rex S, Munafò MR, & Taylor G (2020). Family meal frequency, diet, and family functioning: A systematic review with meta-analyses. Journal of Nutrition Education and Behavior, 52(5), 553–564. 10.1016/j.jneb.2019.12.012 [DOI] [PubMed] [Google Scholar]
  49. Selman SB, & Dilworth-Bart JE (2024). Routines and child development: A systematic review. Journal of Family Theory & Review, 16(2), 272–328. 10.1111/jftr.12549 [DOI] [Google Scholar]
  50. Shek DT, Dou D, Zhu X, & Chai W (2019). Positive youth development: Current perspectives. Adolescent Health, Medicine and Therapeutics, Volume 10, 131–141. 10.2147/AHMT.S179946 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Shirzad K, Bai S, & Agans JP (2024). Development amidst psychological difficulties: Parents as developmental assets. Journal of Youth Development, 19(3). [Google Scholar]
  52. Shrout PE, & Bolger N (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445. 10.1037/1082-989X.7.4.422 [DOI] [PubMed] [Google Scholar]
  53. Song Q, Su W, Li N, Wang H, Guo X, Liang Q, Qu G, Ding X, Liang M, Qin Q, Chen M, Sun L, & Sun Y (2023). Family function and emotional behavior problems in Chinese children and adolescents: A moderated mediation model. Journal of Affective Disorders, 341, 296–303. 10.1016/j.jad.2023.08.138 [DOI] [PubMed] [Google Scholar]
  54. Spagnola M, & Fiese BH (2007). Family routines and rituals: A context for development in the lives of young children. Infants & Young Children, 20(4), 284–299. 10.1097/01.IYC.0000290352.32170.5a [DOI] [Google Scholar]
  55. Walsh F (Ed.). (2012). Normal family processes: Growing diversity and complexity. Guilford Press. [Google Scholar]
  56. Welsh EM, French SA, & Wall M (2011). Examining the relationship between family meal frequency and individual dietary intake: Does family cohesion play a role? Journal of Nutrition Education and Behavior, 43(4), 229–235. 10.1016/j.jneb.2010.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. White J, & Halliwell E (2010). Alcohol and tobacco use during adolescence: The importance of the family mealtime environment. Journal of Health Psychology, 15(4), 526–532. 10.1177/1359105309355337 [DOI] [PubMed] [Google Scholar]
  58. Wickersham A, Sugg HVR, Epstein S, Stewart R, Ford T, & Downs J (2021). Systematic review and meta-analysis: The association between child and adolescent depression and later educational attainment. Journal of the American Academy of Child & Adolescent Psychiatry, 60(1), 105–118. 10.1016/j.jaac.2020.10.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Winship S, & O’Rourke T (2024, March 12). Family Dinners Offer a Silver Lining in a Bleak Social Capital Landscape. Institute for Family Studies. https://ifstudies.org/blog/family-dinners-offer-a-silver-lining-in-bleak-social-capital-landscape [Google Scholar]

RESOURCES