The association between the Healthy Eating Index (HEI-2015) score and body composition among Iranian soccer players and referees: a cross-sectional study

For an optimal performance, soccer players and referees need to consume a high-quality diet. The Healthy Eating Index (HEI) is a tool that can estimate diet quality and has been shown to be associated with body composition. The aims of the present study were first to determine the HEI-2015 score of the diets consumed by athletes and second its association with different body composition parameters of athletes. We conducted a cross-sectional study on 198 soccer players and referees. Dietary intakes were recorded using a validated food frequency questionnaire (FFQ), and HEI scores were calculated. Body composition parameters were measured using the bioelectrical impedance analysis. The mean score for the HEI-2015 was 65⋅04. A multiple linear regression model showed significant associations of the HEI-2015 score with percent body fat (PBF), percent muscle mass (PMM), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) in male soccer players aged <18 years, body mass index (BMI) in male soccer players aged ≥18 years and BMI and waist-to-height ratio (WHtR) in male soccer referees after adjustment for covariates (P < 0⋅05). The mean overall score for the HEI-2015 shows that Iranian soccer players and referees have an acceptable quality of diet. We also found significant associations between the HEI-2015 score and different body composition parameters in male soccer players and referees but we did not find any significant association in female athletes (P > 0⋅05).

Association of adherence to the Australian Dietary Guidelines with cognitive performance and cognitive decline in the Sydney Memory and Ageing Study: a longitudinal analysis

This study investigated associations of adherence to the Australian Dietary Guidelines (ADG) with cognitive performance and cognitive decline over 6 years. We used longitudinal data from the Sydney Memory and Aging Study comprising 1037 community-dwelling non-demented participants aged 70–90 years. Dietary intake was assessed at baseline using the Dietary Questionnaire for Epidemiological Studies Version 2. Adherence to the ADG was scored using the Dietary Guideline Index 2013 (DGI-2013). Cognition was assessed using neuropsychological tests in six cognitive domains and global cognition at baseline and 2, 4 and 6 years later. Linear mixed models analysed the association between adherence to the ADG and cognitive function and cognitive decline over 6 years. Results indicated that overall adherence to the ADG was suboptimal (DGI-2013 mean score 43⋅8 with a standard deviation of 10⋅1; median score 44, range 12–73 with an interquartile range of 7). The percent of participants attaining recommended serves for the five food groups were 30⋅2 % for fruits, 11⋅2 % for vegetables, 54⋅6 % for cereals, 28⋅9 % for meat and alternatives and 2⋅1 % for dairy consumption. Adherence to the ADG was not associated with overall global cognition over 6 years (β = 0⋅000; 95 % CI: −0⋅007, 0⋅007; P = 0⋅95). Neither were DGI-2013 scores associated with change in global cognitive performance over 6 years (β = 0⋅002; 95 % CI: −0⋅002, 0⋅005; P = 0⋅41) nor in any individual cognitive domains. In conclusion, adherence to the ADG was not associated with cognitive health over time in this longitudinal analysis of older Australians. Future research is needed to provide evidence to support specific dietary guidelines for neurocognitive health among Australian older adults.

Impact of physical activity, diet quality and stress on cardiometabolic health in school employees

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School employee health and well-being directly impact student learning and success.

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Higher physical activity levels were associated with better cardiometabolic health.

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High stress had a negative impact on the relationship between PA and BMI.

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Stress may affect heart health, potentially through an impact on health behaviors.

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Enhancing school employee well-being may improve student academic achievement.

Among school employees, it has been reported that poor physical and mental health, as well as high stress and large workloads, have resulted in high absenteeism and low retention. The consequences of unhealthy behaviors and stress can extend to students, impacting academic achievement and school costs. Our objective was to examine the impact of school employees’ physical activity (PA), diet quality and perceived occupational stress on cardiometabolic health, and explore how stress may influence the impact of PA and diet on health.

In this cross-sectional study, employees from lower-income Massachusetts schools participated in Wellness Assessments (2015–2016), including measured height, weight, and lipids [total (TC), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C)]. Self-administered surveys were used to collect demographic, stress, PA and 24-hour food intake data. Linear regression models were used to examine the relationship among health behaviors (PA and diet), stress and cardiometabolic health. An interaction between stress and health behaviors was also explored.

Seventy-four employees (66% teachers) participated. Overweight/obesity (mean BMI: 25.6 kg/m²), high TC and LDL-C were observed in 47%, 4%, and 34%, respectively, and moderate-to-vigorous PA (MVPA) was low (median: 17 min/day). Positive associations were identified between MVPA and cardiometabolic health, but not diet. The effect of MVPA on BMI was modified by stress (p-for-interaction = 0.001), with higher levels of stress associated with a diminished protective association between MVPA and BMI.

Higher levels of PA were associated with more favorable cardiometabolic health, with increasing levels of stress minimizing the beneficial effect of PA on BMI.

The association between maternal depressive symptomology and child dinner dietary quality among Hispanic Head Start families

Dietary quality is important for children’s growth and development. Poor dietary quality and maternal depression are prevalent among low-income, Hispanic families. Maternal depression likely influences child feeding before and during the meal. This secondary data analysis of an observational feeding study (2007–2008) examined how maternal depressive symptomology relates to dietary quality of dinner served to and consumed by Head Start preschoolers in Houston, TX (n = 82 mother-child dyads). A digital photography method assessed food served and consumed by the child at three separate dinner meals in families’ homes. Healthy Eating Index-2010 (HEI) was calculated and averaged across the three meals to measure dietary quality (possible range 0–100). Maternal depression was assessed by the Centers for Epidemiologic Depression Scale (CES-D, possible range 0–60). A series of linear regression models were developed, regressing the total CES-D score and all four CES-D subscales onto both the dietary quality of the meal served and consumed. Dinners served had a HEI of 45.70 ± 9.19 and dinners consumed had a HEI of 44.65 ± 7.34. Clinically significant depressive symptomology (CES-D ≥ 16) was reported by 28% of mothers. Maternal depressive symptomology and the dietary quality served were not related. Controlling for dietary quality served, total CES-D and somatic complaints subscale scores were associated with lower dietary quality consumed (respectively, β = −0.16, p < 0.05 and β = −0.23, p < 0.01). Among low-income, Hispanic families, maternal depressive symptomology was predictive of the dietary quality consumed, but not served. Together, these findings reinforce the importance of parent feeding behaviors and emotional climates during dinner.

Examining disparities in diet quality between SNAP participants and non-participants using Oaxaca-Blinder decomposition analysis

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SNAP participants had poorer diet quality compared to non-participants.

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The HEI-2015 disparity between SNAP participants and income-eligible non-participants was 3.24.

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Demographics explained 36% of the disparity in diet quality between SNAP participants and income-eligible non-participants.

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The HEI-2015 disparity between SNAP participants and income-ineligible non-participants was 6.30.

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Demographics explained 72% of the disparity in diet quality between SNAP participants and income-ineligible non-participants.

Recent studies have reported that SNAP participants have poorer diet quality than non-participants. This study aimed to examine how differences in socio-demographic, household, and health-related measures explain disparities in diet quality between SNAP participants and non-participants using Oaxaca-Blinder decomposition analysis.

We analyzed cross-sectional data on 14,331 adult respondents of the National Health and Nutrition Examination Survey (NHANES) 2009 – 2014. To measure diet quality, we applied the Healthy Eating Index (HEI)-2015 to respondents’ 24-hour dietary recall data (scale: 0–100 points). We used Oaxaca-Blinder decomposition analysis to determine how much of the disparity in HEI-2015 total score between SNAP participants and non-participants was explained by socio-demographic (e.g., age, race/ethnicity, educational), household (e.g., household size, food security status), and health-related measures (e.g., BMI, smoking status).

Analyses performed revealed significant differences in HEI-2015 total score by SNAP participation status (p < 0.001). We found that the total gap in HEI-2015 total score between SNAP participants and income-ineligible non-participants was 6.30 points. Socio-demographic measures alone explained 72.40% of the disparity. All measures together explained 86.31% of the disparity. The total gap between SNAP participants and income-eligible non-participants was 3.24 points. Socio-demographic measures alone explained 35.51% of this disparity while all measures together explained 56.86%.

We observed disparities in diet quality between SNAP participants and non-participants. Socio-demographic, household, and health-related measures explained a significant amount of the disparity that existed between SNAP participants and income-ineligible non-participants; they explained less of the disparity between SNAP participants and income-eligible non-participants.