Gender differences in diet quality and the association between diet quality and BMI: an analysis in young Australian adults who completed the Healthy Eating Quiz.

BACKGROUND: Many young adults report poor diet quality. However, research evaluating whether young adult males and females differ in diet quality is limited. Additionally, although diet quality has a known inverse association with body mass index (BMI), it is unclear whether this association is observed in young adults and whether it varies by gender. The present study aimed to evaluate gender differences in diet quality in young adults, as well as the associations between diet quality and BMI. METHODS: Data collected via the Healthy Eating Quiz (HEQ) in respondents aged 18-35 years between July 2019 and December 2021 were analysed, including demographics, and diet quality calculated using the Australian Recommended Food Score (ARFS). Differences in characteristics were analysed using a two-sample t-test, chi-squared and one-way analysis of covariance. Linear regressions were performed to estimate associations between diet quality and BMI. An interaction term was included in the model to test differences between genders. RESULTS: The respondents (n = 28,969) were predominantly female (70.8%) with a mean ± SD age of 25.9 ± 5.0 years and BMI of 24.6 ± 5.2 kg/m2. The mean ± SD ARFS was significantly different between females and males (33.1 ± 8.6 vs. 31.4 ± 9.3 points out of 70; p < 0.001). Diet quality had a small, significant inverse association with BMI in both genders. The interaction effect between diet quality score and gender in predicting BMI was significant (p < 0.001), suggesting the impact of diet quality on BMI varies by gender, with lower diet quality more strongly associated with higher BMI in females compared to males. CONCLUSION: Interventions that target young adults are needed to improve diet quality and its potential contribution to BMI status. As a result of the small observed effect sizes, caution should be applied in interpreting these findings.

Are health behaviors associated with mental health among tertiary education students? A systematic review of cohort studies.

OBJECTIVE: To evaluate the association between health behaviors with mental health among tertiary education students. METHODS: Six databases were searched until September 2021 for prospective cohort studies evaluating the association between health behavior(s) (dietary intake, physical activity, sedentary behavior, alcohol intake, sleep, smoking or illicit drug use) and mental health. Two independent reviewers screened records for inclusion, extracted data and completed risk of bias assessments. RESULTS: 33 studies were included (14 assessed sleep, 14 alcohol intake, 13 physical activity, 8 smoking, 6 sedentary behavior, 4 diet, 1 illicit drug use). A consistent association between poor sleep, and physical inactivity with increased risk of poor psychological wellbeing, and between poor sleep and increased mental ill-health related outcomes was demonstrated. CONCLUSION: Findings suggest interventions to address poor sleep and physical inactivity among students may positively impact mental health. Further research of other health behaviors, and their association with mental health, is required.

Changes in physical activity, diet, sleep, and mental well-being when starting university: A qualitative exploration of Australian student experiences.

Objective: To explore young adult's experiences of how starting university influenced their physical activity, diet, sleep, and mental well-being, and barriers and enablers to health behavior change. Participants: University students aged 18-25 years. Methods: Three focus groups were conducted in November 2019. Inductive thematic approach was utilized to identify themes. Results: Students (female: n = 13, male: n = 2, other gender identity: n = 1, 21.2 (1.6) years) reported mental well-being, physical activity levels, diet quality and sleep health were negatively affected. Stress, study demands, university timetabling, not prioritizing physical activity, cost and availability of healthy foods, and difficulty falling asleep were key barriers. Health behavior change interventions aiming to support mental well-being need to include both information and support features. Conclusions: There is a significant opportunity to improve the transition to university for young adults. Findings highlight areas to target in future interventions to improve physical activity, diet, and sleep of university students.

Health behavior interventions for university students measuring mental health outcomes: A scoping review.

Introduction: Many university students have poor mental health, and co-occurring health risk behaviors. Targeting health behavior change in this population may improve mental health outcomes. This scoping review describes the extent and range of randomized controlled trials (RCT) evaluating interventions targeting health risk behaviors and measuring a mental health outcome, among university students. Methods: Six electronic databases were searched for RCTs published until the 18th May 2021. Eligible RCTs included university students, evaluated interventions that promoted health behavior change (i.e., dietary intake, physical activity, sedentary behavior, alcohol and drug use, smoking, and sleep), and measured a mental health-related outcome. Results: Fifty-nine RCTs met the inclusion criteria that were published from 2000 to 2021, and over half (n = 33) were conducted in the United States. Interventions evaluated within the RCTs (n = 92) predominantly targeted changes to dietary intake (n = 41 interventions), physical activity (n = 39), or alcohol intake (n = 35). Most interventions targeted one (n = 51) or two (n = 27) health behaviors only. Included RCTs considered mental ill health outcomes (n = 24), psychological wellbeing outcomes (n = 20), or both (n = 15). Discussion: This scoping review identified a moderate volume of experimental research investigating the impact of health behavior interventions on university students' mental health. There is scope for further research examining health behavior interventions targeting university students, particularly interventions taking a multi-behavioral approach.

Gender differences in social desirability and approval biases, and associations with diet quality in young adults.

Social desirability and approval biases can influence self-report of diet, contributing to inaccurate assessment. Biases may be influenced by sex. This study examined gender differences in social desirability and approval, and diet quality in young adults, and relationships between social biases and diet quality. Data from 1003 young adults participating in the Advice, Ideas and Motivation for My Eating trial, median age 21.7 years, BMI 24.7 kg/m2 were included. Biases were measured using Marlowe-Crowne Social Desirability Scale Short-Form, Martin Larsen Approval Motivation Scale, and diet quality using the Australian Recommended Food Score. Pearson chi-square, t-test and Wilcoxon tests evaluated gender differences and Spearman's rank-order tested strengths of correlations between biases and diet quality. Social desirability did not differ by gender, however females reported higher social approval scores than males (p = 0.009). Among females, there was a positive correlation between social desirability and diet quality (p = 0.002), and between social approval and dairy sub-scale (p < 0.001); and a negative correlation between social approval and vegetable subscale (p = 0.045). Among males there were no correlations between social desirability or social approval and overall diet quality. Social approval may be more common in females and influence self-reported diet. To address bias and mis-reporting errors, future studies should account for participants' social desirability and/or social approval status, especially among young adult females. Further research is needed to explore gender differences in social desirability and approval in young adults, and associations between social biases and diet quality.

Efficacy of a Multi-Component m-Health Diet, Physical Activity, and Sleep Intervention on Dietary Intake in Adults with Overweight and Obesity: A Randomised Controlled Trial.

This three-arm randomised controlled trial evaluated whether (1) a multi-component weight loss intervention targeting diet, physical activity (PA), and sleep was effective at improving dietary intake over six months and 12 months, compared with a control, and (2) the enhanced diet, PA, and sleep intervention was more effective at improving dietary intake than the traditional diet and PA intervention. A total of 116 adults (70% female, 44.5 years, BMI 31.7 kg/m2) were randomised to either traditional diet and PA intervention; enhanced diet, PA, and sleep intervention; or wait-list control. To examine between-group differences, intervention groups were pooled and compared with the control. Then, the two intervention groups were compared. At six months, the pooled intervention group consumed 1011 fewer kilojoules/day (95% CI -1922, -101), less sodium (-313.2 mg/day; 95% CI -591.3, -35.0), and higher %EI from fruit (+2.1%EI; 95% CI 0.1, 4.1) than the controls. There were no differences in intake between the enhanced and traditional groups at six months. At 12 months, the pooled intervention and control groups reported no significant differences. However, compared to the traditional group, the enhanced reported higher %EI from nutrient-dense foods (+7.4%EI; 95% CI 1.3, 13.5) and protein (+2.4%EI; 95% CI 0.1, 4.6), and reduced %EI from fried/takeaway foods (-3.6%EI; 95% CI -6.5, -0.7), baked sweet products (-2.0%EI; 95% CI -3.6, -0.4), and packaged snacks (-1.1%EI; 95% CI -2.2, -0.3). This weight loss intervention reduced total energy and sodium intakes as well as increased fruit intake in adults at six months. The enhanced intervention group reported improved dietary intake relative to the traditional group at 12 months.

Behavioural mediators of reduced energy intake in a physical activity, diet, and sleep behaviour weight loss intervention in adults.

Reduced energy intake is a major driver of weight loss and evidence suggests that physical activity, dietary, and sleep behaviours interact to influence energy intake. Energy restriction can be challenging to sustain. Therefore to improve intervention efficacy, evaluation of how changes in physical activity, diet, and sleep behaviours mediate reduced energy intake in adults with overweight/obesity who participated in a six-month multiple-behaviour-change weight loss intervention was undertaken. This was a secondary analysis of a 3-arm randomised controlled trial. Adults with body mass index (BMI) 25-40 kg/m2 were randomised to either: a physical activity and diet intervention; physical activity, diet, and sleep intervention; or wait-list control. Physical activity, dietary intake, and sleep was measured at baseline and six-months using validated measures. The two intervention groups were pooled and compared to the control. Structural equation modelling was used to estimate the mediated effects (AB Coefficient) of the intervention on total energy intake. One hundred and sixteen adults (70% female, 44.5y, BMI 31.7 kg/m2) were enrolled and 70% (n = 81) completed the six-month assessment. The significant intervention effect on energy intake at six-months (-1011 kJ/day, 95% CI -1922, -101) was partially mediated by reduced fat intake (AB = -761.12, 95% CI -1564.25, -53.74) and reduced consumption of energy-dense, nutrient-poor foods (AB = -576.19, 95% CI -1189.23, -97.26). In this study, reducing fat intake and consumption of energy-dense, nutrient-poor foods was an effective strategy for reducing daily energy intake in adults with overweight/obesity at six-months. These strategies should be explicitly targeted in future weight loss interventions.

Sleep, diet, activity, and incident poor self-rated health: A population-based cohort study.

OBJECTIVE: The prospective relationships between poor sleep health, poor diet quality, and physical inactivity with self-rated health (SRH) are not well described. The aim of this study was to assess individual and joint associations between high-risk health behaviors and incident poor SRH. METHOD: Participants from the Household Income and Labor Dynamics in Australia longitudinal cohort reporting "good" SRH in 2013 were included (n = 8,853) in 2020 data analysis. Logistic regression was used to assess odds of poor SRH in 2017 associated with (a) individual, (b) count, and (c) unique combinations of high-risk behaviors reported in 2013. RESULTS: In the sample (48% female, Mage = 45.2 years, SD = 16.8), poor sleep health (OR = 1.66, 95% CI [1.38, 2.01]), physical inactivity (OR = 1.18, [1.01, 1.38]), and poor diet quality (OR = 1.38, [1.16, 1.65]) were associated with increased odds of poor SRH. Reporting one (OR = 1.76, [1.27, 2.43]), two (OR = 2.16, [1.57, 2.98]), and three (OR = 2.99, [2.02, 4.41]) high-risk behaviors was associated with increased odds of poor SRH. All unique combinations of high-risk behaviors were significantly associated with greater odds of poor SRH, except "poor sleep health only" (prevalence = 1.3%). Odds of poor SRH associated with high-risk behavior combinations ranged from 1.73 (95% CI [1.21, 2.47]) for "physical inactivity only" to 4.11 ([2.66, 6.35]) for "poor sleep health + poor diet quality." CONCLUSIONS: Reporting ≥ 1 high-risk behavior was associated with increased odds of poor SRH. The combination of poor sleep health with poor diet quality was associated with the greatest odds of poor SRH. Improving multiple high-risk behaviors in combination may be more effective in preventing decline in SRH than improving any behavior alone. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Efficacy of a Multi-component m-Health Weight-loss Intervention in Overweight and Obese Adults: A Randomised Controlled Trial.

BACKGROUND: This study compared the efficacy of two multi-component m-health interventions with a wait-list control group on body weight (primary outcome), and secondary outcomes of cardiovascular risk factors, lifestyle behaviours, and mental health. METHODS: Three-arm randomised controlled trial (Enhanced: physical activity, diet, sleep, Traditional: physical activity, diet, Control) with assessments conducted at baseline, 6 and 12 months. Participants (n = 116) were overweight or obese adults aged 19-65 (M = 44.5 [SD = 10.5]). The 6-month intervention was delivered via a smartphone app providing educational materials, goal-setting, self-monitoring and feedback, and also included one face-to-face dietary consultation, a Fitbit and scales. The trial was prospectively registered and conducted between May 2017 and September 2018. Group differences on primary and secondary outcomes were examined between the Pooled Intervention groups (Pooled Intervention = Enhanced and Traditional) and Control groups, and then between Enhanced and Traditional groups. RESULTS: Nineteen participants (16.4%) formally withdrew from the trial. Compared with the Control group, average body weight of the Pooled Intervention group did not differ at 6 (between-group difference = -0.92, (95% CI -3.33, 1.48)) or 12 months (0.00, (95% CI -2.62, 2.62)). Compared with the Control group, the Pooled Intervention group significantly increased resistance training (OR = 7.83, (95% CI 1.08, 56.63)) and reduced energy intake at 6 months (-1037.03, (-2028.84, -45.22)), and improved insomnia symptoms at 12 months (-2.59, (-4.79, -0.39)). Compared with the Traditional group, the Enhanced group had increased waist circumferences (2.69, (0.20, 5.18)) and sedentary time at 6 months (105.66, (30.83, 180.48)), and improved bed time variability at 12 months (-1.08, (-1.86, -0.29)). No other significant differences were observed between groups. CONCLUSIONS: Relative to Controls, the Pooled Intervention groups did not differ on body weight but improved resistance training, and reduced energy intake and insomnia symptom severity. No additional weight loss was apparent when targeting improvements in physical activity, diet and sleep in combination compared with physical activity and diet.

Feasibility and Preliminary Efficacy of an m-Health Intervention Targeting Physical Activity, Diet, and Sleep Quality in Shift-Workers.

Poor health behaviors are prevalent in shift-workers, but few multiple health-behavior interventions consider their unique needs. This study aimed to (1) evaluate the feasibility and acceptability of an existing app-based intervention to improve physical activity, diet, and sleep quality in a shift-worker population, (2) estimate intervention effect in a four-week pilot randomized controlled trial (RCT) (ACTRN12618001785291). Shift-workers (18-65 years old) were randomized to intervention (n = 20) or wait-list (n = 20) groups. Outcomes included recruitment, engagement, attrition, usefulness ratings, System Usability Scale (SUS), qualitative interviews, and estimation of treatment effect (minutes of physical activity, diet quality, and sleep quality) using mixed model analysis. Recruitment took one week. App-use at week four was 55% (11/20), 85% (34/40) completed the four-week follow-up questionnaire, and 20% (4/20) of the intervention group completed the qualitative interview. The intervention was rated as slightly to moderately useful by 76.9% (10/13) of participants on a five-point scale. The SUS score was 62.7 (12.7) out of 100. Diet quality improved for the intervention (4.5 points; 95% confidence interval (CI) = 0.1, 8.9; p = 0.047) vs. the wait-list group, but not physical activity or sleep quality. Qualitative interviews found that a more tailored intervention, more accessible information, and interactive features were desired. The intervention was feasible in terms of recruitment, but modifications to increase engagement are needed.

Examining the efficacy of a multicomponent m-Health physical activity, diet and sleep intervention for weight loss in overweight and obese adults: randomised controlled trial protocol.

INTRODUCTION: Traditional behavioural weight loss trials targeting improvements in physical activity and diet are modestly effective. It has been suggested that sleep may have a role in weight loss and maintenance. Improving sleep health in combination with physical activity and dietary behaviours may be one strategy to enhance traditional behavioural weight loss trials. Yet the efficacy of a weight loss intervention concurrently targeting improvements in physical activity, dietary and sleep behaviours remains to be tested. METHODS AND ANALYSIS: The primary aim of this three-arm randomised controlled trial is to examine the efficacy of a multicomponent m-Health behaviour change weight loss intervention relative to a waitlist control group. The secondary aims are to compare the relative efficacy of a physical activity, dietary behaviour and sleep intervention (enhanced intervention), compared with a physical activity and dietary behaviour only intervention (traditional intervention), on the primary outcome of weight loss and secondary outcomes of waist circumference, glycated haemoglobin, physical activity, diet quality and intake, sleep health, eating behaviours, depression, anxiety and stress and quality of life. Assessments will be conducted at baseline, 6 months (primary endpoint) and 12 months (follow-up). The multicomponent m-Health intervention will be delivered using a smartphone/tablet 'app', supplemented with email and SMS and individualised in-person dietary counselling. Participants will receive a Fitbit, body weight scales to facilitate self-monitoring, and use the app to access educational material, set goals, self-monitor and receive feedback about behaviours. Generalised linear models using an analysis of covariance (baseline adjusted) approach will be used to identify between-group differences in primary and secondary outcomes, following an intention-to-treat principle. ETHICS AND DISSEMINATION: The Human Research Ethics Committee of The University of Newcastle Australia provided approval: H-2017-0039. Findings will be disseminated via publication in peer-reviewed journals, conference presentations, community presentations and student theses. TRIAL REGISTRATION NUMBER: ACTRN12617000735358; UTN1111-1219-2050.

Relationship between dietary intake and behaviors with oxytocin: a systematic review of studies in adults.

Context: Oxytocin plays an important hormonal role in the regulation of feeding and energy intake. Objective: The aims of this review were to 1) determine the effects of dietary intake/behaviors on endogenous oxytocin and 2) examine the effect of exogenous oxytocin on dietary intake/behaviors. Data sources: Published studies up to December 2016 were identified through searches of 5 electronic databases. Data extraction: Eligible studies included those in adults that included a measure related to an individual's diet and a measure of oxytocin and the relationship between the 2 outcomes. Results: Twenty-six studies (n = 912 participants; 77% female) were included. The most common dietary outcomes assessed were alcohol, caffeine, calcium, sodium, fat, and calorie intake. It was found that endogenous oxytocin (n = 13) in nonclinical samples did not change significantly (P > 0.05) through altered diet or behaviors (neutral effect); in contrast, significant (P < 0.05) differences (increases and decreases) were identified in clinical samples. Exogenous oxytocin studies (n = 13) found reduced indices of food intake (positive effect) in clinical and nonclinical samples. Conclusions: Overall, few studies included comprehensive investigation of dietary intakes through the use of validated assessment tools. Dietary intake and behaviors appear to have some influence on oxytocin, with more pronounced effects found with exogenously administered oxytocin.

Balanced: a randomised trial examining the efficacy of two self-monitoring methods for an app-based multi-behaviour intervention to improve physical activity, sitting and sleep in adults.

BACKGROUND: Many adults are insufficiently physically active, have prolonged sedentary behaviour and report poor sleep. These behaviours can be improved by interventions that include education, goal setting, self-monitoring, and feedback strategies. Few interventions have explicitly targeted these behaviours simultaneously or examined the relative efficacy of different self-monitoring methods. METHODS/DESIGN: This study aims to compare the efficacy of two self-monitoring methods in an app-based multi-behaviour intervention to improve objectively measured physical activity, sedentary, and sleep behaviours, in a 9 week 2-arm randomised trial. Participants will be adults (n = 64) who report being physically inactive, sitting >8 h/day and frequent insufficient sleep (≥14 days out of last 30). The "Balanced" intervention is delivered via a smartphone 'app', and includes education materials (guidelines, strategies to promote change in behaviour), goal setting, self-monitoring and feedback support. Participants will be randomly allocated to either a device-entered or user-entered self-monitoring method. The device-entered group will be provided with a activity tracker to self-monitor behaviours. The user-entered group will recall and manually record behaviours. Assessments will be conducted at 0, 3, 6, and 9 weeks. Physical activity, sedentary behaviour and sleep-wake behaviours will be measured using the wrist worn Geneactiv accelerometer. Linear mixed models will be used to examine differences between groups and over time using an alpha of 0.01. DISCUSSION: This study will evaluate an app-based multi-behavioural intervention to improve physical activity, sedentary behaviour and sleep; and the relative efficacy of two different approaches to self-monitoring these behaviours. Outcomes will provide information to inform future interventions and self-monitoring targeting these behaviours. TRIAL REGISTRATION: ACTRN12615000182594 (Australian New Zealand Clinical Trials Registry. Registry URL: www.anzctr.org.au ; registered prospectively on 25 February 2015).