The Weight of Time: Time influences on overweight and obesity in women

We know that adults’ weight increases with age, at least until around the age of 55 years or older. Recent National Australian surveys show that men and women of all age groups were heavier in 2000 than in 1995 or 1990. These studies also found that a greater proportion of people of all ages were overweight or obese in 2000 than in the previous surveys. These studies also suggested that different generations, also known as ‘birth cohorts’, had different patterns of weight gain. These birth cohort influences mean that the year a person is born and the unique set of experiences people born at that time experience, have an effect on weight gain patterns. People born at other times experience different conditions and have different weight gain patterns. Some birth cohorts or ‘generations’ are well-known, such as the ‘baby boomer’ generation, or pre-war generation. For example, Australians born in the first three decades of the twentieth century experienced World War I and II and the Great Depression during their childhood and early adult life. During these times food was scarce and everyday life required high levels of physical activity. This group overall had lower body weights than more recent generations, meaning they were less at risk of becoming obese. Australians born after 1980 were born into an advanced technological society with greater availability of food, a vastly increased range of food products and increasing serving sizes. At the same time, levels of physical activity in everyday life have been decreasing. Together these factors produce an obesogenic environment. The three National Health surveys, conducted by the Australian Bureau of Statistics for the Australian Institute of Health and Welfare in 1990, 1995 and 2000, produced data which the NSW Centre for Overweight and Obesity has analyzed to find out what effects three time factors -- ageing, the time of the surveys and birth cohort, have on body mass index (BMI) and the prevalence of overweight and obesity. [Note – BMI used as the indicator of weight status, where BMI = weight (kg)/height2 (M2)] This report provides an overview of key findings of the analyses of the effects of these three time factors on female weight patterns. The complete findings have been published in a comprehensive technical report. The results from the analysis of the effect of birth cohorts have been used to predict the mean body mass index of women in NSW in 2010. The graphs in this report show the results for women, and the results for men are available in a separate document. The overall patterns and implications for men are generally similar to those for women. NS

The Weight of Time: Time influences on overweight and obesity in men

We know that adults’ weight increases with age, at least until around the age of 55 years or older. Recent National Australian surveys show that men and women of all age groups were heavier in 2000 than in 1995 or 1990. These studies also found that a greater proportion of people of all ages were overweight or obese in 2000 than in the previous surveys. These studies also suggested that different generations, also known as ‘birth cohorts’, had different patterns of weight gain. These birth cohort influences mean that the year a person is born and the unique set of experiences people born at that time experience, have an effect on weight gain patterns. People born at other times experience different conditions and have different weight gain patterns. Some birth cohorts or ‘generations’ are well-known, such as the ‘baby boomer’ generation, or pre-war generation. For example, Australians born in the first three decades of the twentieth century experienced World War I and II and the Great Depression during their childhood and early adult life. During these times food was scarce and everyday life required high levels of physical activity. This group overall had lower body weights than more recent generations, meaning they were less at risk of becoming obese. Australians born after 1980 were born into an advanced technological society with greater availability of food, a vastly increased range of food products and increasing serving sizes. At the same time, levels of physical activity in everyday life have been decreasing. Together these factors produce an obesogenic environment. The three National Health surveys, conducted by the Australian Bureau of Statistics for the Australian Institute of Health and Welfare in 1990, 1995 and 2000, produced data which the NSW Centre for Overweight and Obesity has analyzed to find out what effects three time factors -- ageing, the time of the surveys and birth cohort -- have on body mass index (BMI) and the prevalence of overweight and obesity. [Note – BMI used as the indicator of weight status, where BMI = weight (kg)/height2 (M2)] This report provides an overview of key findings of the analyses of the effects of these three time factors on male weight patterns. The complete findings have been published in a comprehensive technical report. The results from the analysis of the effect of birth cohorts have been used to predict the mean body mass index of men in NSW in 2010. The graphs in this report show the results for men but the results for women are available. The overall patterns and implications for women are generally similar to those for men

Effects of Nutritional Interventions on Cardiovascular Disease Health Outcomes in Aboriginal and Torres Strait Islander Australians: A Scoping Review.

Nutrition interventions can support Aboriginal and Torres Strait Islander peoples to reduce their risk of cardiovascular disease (CVD). This review examines nutritional interventions aiming to improve CVD outcomes and appraises peer-reviewed interventions using an Aboriginal and Torres Strait Islander Quality Appraisal Tool. Five electronic databases and grey literature were searched, applying no time limit. Two reviewers completed the screening, data extraction and quality assessment independently. The study quality was assessed using the South Australian Health and Medical Research Institute and the Centre of Research Excellence in Aboriginal Chronic Disease Knowledge Translation and Exchange Aboriginal and Torres Strait Islander Quality Appraisal Tool (QAT). Twenty-one nutrition programs were included in this review. Twelve reported on anthropometric measurements, ten on biochemical and/or hematological measurements and sixteen on other outcome domains. Most programs reported improvements in measurable CVD risk factors, including reduced body mass index (BMI), waist circumference (WC), weight, blood pressure and improved lipid profiles. Most programs performed well at community engagement and capacity strengthening, but many lacked the inclusion of Indigenous research paradigms, governance and strengths-based approaches. This review highlights the need for contemporary nutrition programs aimed at improving cardiovascular health outcomes to include additional key cultural components

A virtual reality food court to study meal choices in youth: design and assessment of usability

BACKGROUND: Regular consumption of take-out and fast foods with sugary drinks is associated with poor quality diets and higher prevalence of obesity. Among the settings where such food is consumed is the food court typically found in shopping malls prominent in many countries. OBJECTIVE: The objective of this research was to develop a virtual reality food court that could be used to test food environmental interventions, such as taxation, and ultimately to facilitate the selection of healthier food choices. METHODS: Fourteen food courts in Sydney, Australia were selected to include those in the city center and suburbs of high and low socioeconomic status. Researchers visited the courts to collect information on number and type of food outlets, all menu items for sale, cost of foods and beverages and sales promotions. This information was used to assemble 14 food outlets typically found in food courts, and representative menus were compiled. The UNITY gaming platform was used to design a virtual reality food court that could be used with HTC VIVE goggles. Participants navigated the virtual reality food court using the head-mounted display, keyboard, and mouse and selected a lunch meal, including food and beverage. A validated questionnaire on presence within the virtual reality food court and system usability was completed at the end of the session. The constructs for presence included a sense of control, sensory fidelity, realism, distraction, and involvement. Questions were rated on a scale from 1 (worst) through 7 (best) for each of 28 questions giving a maximum total score of 196. The systems usability scale (SUS) that gives a final score out of 100 was also assessed. RESULTS: One hundred and sixty-two participants with a mean age of 22.5 (SD 3.1) years completed the survey. The mean score for total presence was 144 (SE 1.4) consisting of control: 62.1 (SE 0.8), realism: 17.5 (SE 0.2), involvement: 9.6 (SE 0.2), sensory fidelity: 34.9 (SE 0.4), and distraction: 24.0 (SE 0.3). The mean SUS was 69 (SE 1.1). CONCLUSIONS: Virtual reality shows promise as a tool to study food choice for test interventions to inform practice and policy

Assessment of the methods used to develop vitamin d and calcium recommendations—a systematic review of bone health guidelines

Background: There are numerous guidelines developed for bone health. Yet, it is unclear whether the differences in guideline development methods explain the variability in the recommendations for vitamin D and calcium intake. The objective of this systematic review was to collate and compare recommendations for vitamin D and calcium across bone health guidelines, assess the methods used to form the recommendations, and explore which methodological factors were associated with these guideline recommendations. Methods: We searched MEDLINE, EMBASE, CINAHL, and other databases indexing guidelines to identify records in English between 2009 and 2019. Guidelines or policy statements on bone health or osteoporosis prevention for generally healthy adults aged ≥40 years were eligible for inclusion. Two reviewers independently extracted recommendations on daily vitamin D and calcium intake, supplement use, serum 25 hydroxyvitamin D [25(OH)D] level, and sunlight exposure. They assessed guideline development methods against 25 recommended criteria in the World Health Organization (WHO) handbook for guideline development. Additionally, they identified types of evidence underpinning the recommendations. Results: we included 47 eligible guidelines from 733 records: 74% of the guidelines provided vitamin D (200~600–4000 IU/day) and 70% provided calcium (600–1200 mg/day) recommendations, 96% and 88% recommended vitamin D and calcium supplements, respectively, and 70% recommended a specific 25(OH)D concentration. On average, each guideline met 10 (95% CI: 9–12) of the total of 25 methodological criteria for guideline development recommended by the WHO Handbook. There was uncertainty in the association between the methodological criteria and the proportion of guidelines that provided recommendations on daily vitamin D or calcium. Various types of evidence, including previous bone guidelines, nutrient reference reports, systematic reviews, observational studies, and perspectives/editorials were used to underpin the recommendations. Conclusions: There is considerable variability in vitamin D and calcium recommendations and in guideline development methods in bone health guidelines. Effort is required to strengthen the methodological rigor of guideline development and utilize the best available evidence to underpin nutrition recommendations in evidence-based guidelines on bone health

A Mobile Health Lifestyle Program for Prevention of Weight Gain in Young Adults (TXT2BFiT): Nine-Month Outcomes of a Randomized Controlled Trial

BACKGROUND: The unprecedented rise in obesity among young adults, who have limited interaction with health services, has not been successfully abated. OBJECTIVE: The objective of this study was to assess the maintenance outcomes of a 12-week mHealth intervention on prevention of weight gain in young adults and lifestyle behaviors at 9 months from baseline. METHODS: A two-arm, parallel, randomized controlled trial (RCT) with subjects allocated to intervention or control 1:1 was conducted in a community setting in Greater Sydney, Australia. From November 2012 to July 2014, 18- to 35-year-old overweight individuals with a body mass index (BMI) of 25-31.99 kg/m2 and those with a BMI ≥ 23 kg/m2 and a self-reported weight gain of ≥ 2 kg in the past 12 months were recruited. A 12-week mHealth program "TXT2BFiT" was administered to the intervention arm. This included 5 coaching calls, 96 text messages, 12 emails, apps, and downloadable resources from the study website. Lifestyle behaviors addressed were intake of fruits, vegetables, sugar-sweetened beverages (SSBs), take-out meals, and physical activity. The control group received 1 phone call to introduce them to study procedures and 4 text messages over 12 weeks. After 12 weeks, the intervention arm received 2 further coaching calls, 6 text messages, and 6 emails with continued access to the study website during 6-month follow-up. Control arm received no further contact. The primary outcome was weight change (kg) with weight measured at baseline and at 12 weeks and self-report at baseline, 12 weeks, and 9 months. Secondary outcomes were change in physical activity (metabolic equivalent of task, MET-mins) and categories of intake for fruits, vegetables, SSBs, and take-out meals. These were assessed via Web-based surveys. RESULTS: Two hundred and fifty young adults enrolled in the RCT. Intervention participants weighed less at 12 weeks compared with controls (model β=-3.7, 95% CI -6.1 to -1.3) and after 9 months (model β=- 4.3, 95% CI - 6.9 to - 1.8). No differences in physical activity were found but all diet behaviors showed that the intervention group, compared with controls at 9 months, had greater odds of meeting recommendations for fruits (OR 3.83, 95% CI 2.10-6.99); for vegetables (OR 2.42, 95% CI 1.32-4.44); for SSB (OR 3.11, 95% CI 1.47-6.59); and for take-out meals (OR 1.88, 95% CI 1.07-3.30). CONCLUSIONS: Delivery of an mHealth intervention for prevention of weight gain resulted in modest weight loss at 12 weeks with further loss at 9 months in 18- to 35-year-olds. Although there was no evidence of change in physical activity, improvements in dietary behaviors occurred, and were maintained at 9 months. Owing to its scalable potential for widespread adoption, replication trials should be conducted in diverse populations of overweight young adults. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ANZCTR): ACTRN12612000924853; (Archived by WebCite at http://www.webcitation.org/6i6iRag55)

Effectiveness of a mHealth Lifestyle Program With Telephone Support (TXT2BFiT) to Prevent Unhealthy Weight Gain in Young Adults: Randomized Controlled Trial

BACKGROUND: Weight gained in young adulthood often persists throughout later life with associated chronic disease risk. Despite this, current population prevention strategies are not specifically designed for young adults. OBJECTIVE: We designed and assessed the efficacy of an mHealth prevention program, TXT2BFiT, in preventing excess weight gain and improving dietary and physical activity behaviors in young adults at increased risk of obesity and unhealthy lifestyle choices. METHODS: A two-arm, parallel-group randomized controlled trial was conducted. Subjects and analyzing researchers were blinded. A total of 250 18- to 35-year-olds with a high risk of weight gain, a body mass index (BMI) of 23.0 to 24.9 kg/m(2) with at least 2 kg of weight gain in the previous 12 months, or a BMI of 25.0 to 31.9 kg/m(2) were randomized to the intervention or control group. In the 12-week intervention period, the intervention group received 8 text messages weekly based on the transtheoretical model of behavior change, 1 email weekly, 5 personalized coaching calls, a diet booklet, and access to resources and mobile phone apps on a website. Control group participants received only 4 text messages and printed dietary and physical activity guidelines. Measured body weight and height were collected at baseline and at 12 weeks. Outcomes were assessed via online surveys at baseline and at 12 weeks, including self-reported weight and dietary and physical activity measures. RESULTS: A total of 214 participants—110 intervention and 104 control—completed the 12-week intervention period. A total of 10 participants out of 250 (4.0%)—10 intervention and 0 control—dropped out, and 26 participants (10.4%)—5 intervention and 21 control—did not complete postintervention online surveys. Adherence to coaching calls and delivery of text messages was over 90%. At 12 weeks, the intervention group were 2.2 kg (95% CI 0.8-3.6) lighter than controls (P=.005). Intervention participants consumed more vegetables (P=.009), fewer sugary soft drinks (P=.002), and fewer energy-dense takeout meals (P=.001) compared to controls. They also increased their total physical activity by 252.5 MET-minutes (95% CI 1.2-503.8, P=.05) and total physical activity by 1.3 days (95% CI 0.5-2.2, P=.003) compared to controls. CONCLUSIONS: The TXT2BFiT low-intensity intervention was successful in preventing weight gain with modest weight loss and improvement in lifestyle behaviors among overweight young adults. The short-term success of the 12-week intervention period shows potential. Maintenance of the behavior change will be monitored at 9 months. TRIAL REGISTRATION: Trial Registration: The Australian New Zealand Clinical Trials Registry ACTRN12612000924853; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12612000924853 (Archived by WebCite at http://www.webcitation.org/6Z6w9LlS9)

Validation of a food frequency questionnaire as a tool for assessing dietary intake in cardiovascular disease research and surveillance in Bangladesh

Background: Cardiovascular disease (CVD) has emerged as a major public health concern in Bangladesh. Diet is an established risk factor for CVD but a tool to assess dietary intake in Bangladesh is lacking. This study aimed to validate a food frequency questionnaire (FFQ) using the 24-h dietary recall method and corresponding nutritional biological markers among rural and urban populations of Bangladesh. Method: Participants of both genders aged 18-60 years were included in the analysis (total n = 146, rural n = 94 and urban n = 52). Two FFQs of 166 items were administered three-months apart, during which time three 24-h dietary recalls were also completed. Participants were asked to recall their frequency of consumption over the preceding 3 months. Urine and blood samples were collected for comparison between FFQ-estimates of nutrients and their corresponding biomarkers. Methods were compared using unadjusted, energy-adjusted, de-attenuated correlation coefficients, 95% limits of agreement (LOA) and quartile classification. Results: Fair to moderate agreement for ranking energy, macro and micronutrients into quartiles was observed (weighted k value ranged from 0.22 to 0.58; p < 0.001 for unadjusted data) except for vitamin D (weighted k - 0.05) and zinc (weighted k 0.09). Correlation coefficients of crude energy, macronutrients and common micronutrients including vitamin E, thiamine, riboflavin, niacin, pyridoxine, folate, iron, magnesium, phosphorus, potassium, and sodium were moderately good, ranging from 0.42 to 0.78; p < 0.001 but only fair for vitamin A, β carotene and calcium (0.31 to 0.38; p < 0.001) and poor for vitamin D and zinc (0.02 and 0.16; p = ns, respectively). Energy-adjusted correlations were generally lower except for fat and vitamin E, and in range of - 0.017 (for calcium) to 0.686 (for fat). De-attenuated correlations were higher than unadjusted and energy- adjusted, and significant for all nutrients except for vitamin D (0.017) to 0.801 (for carbohydrate). The Bland Altman tests demonstrated that most of the coefficients were positive which indicated that FFQ provided a greater overestimation at higher intakes. More than one in three participants appeared to overestimate their food consumption based on the ratio of energy intake to basal metabolic rate cut points suggested by Goldberg. Absolute intake of macronutrients was 1.5 times higher and for micronutrients it ranged from 1.07 (sodium) to 26 times (Zinc). FFQ estimates correlated well for sodium (0.32; p < 0.001), and vitamin D (0.20; p = 0.017) with their corresponding biomarkers and iron (0.25; p = 0.003) with serum ferritin for unadjusted data. Folate, iron (with haemoglobin) and total protein showed inverse association; and fat and potassium showed poor correlation with their corresponding biomarkers for unadjusted data. However, folate showed significant positive correlation (0.189; p = 0.025) with biomarker after energy adjustment. Conclusion: Although FFQ showed overestimation for absolute intake in comparison with 24-h recalls, the validation study demonstrated acceptable agreement for ranking dietary intakes from FFQ with 24-h recall methods and some biomarkers and therefore could be considered as a tool to measure dietary intake for research and CVD risk factors surveillance in Bangladesh. The instrument may not be appropriate for monitoring population adherence to recommended intakes because of the overestimation