There are limited methods to assess how dietary patterns adhere to a healthy and sustainable diet. The aim of this study was to develop a theoretically derived Healthy and Sustainable Diet Index ...(HSDI). The HSDI uses 12 components within five categories related to environmental sustainability: animal-based foods, seasonal fruits and vegetables, ultra-processed energy-dense nutrient-poor foods, packaged foods and food waste. A maximum of 90 points indicates the highest adherence. The HSDI was applied to 4-day mobile food records (mFRTM) from 247 adults (18−30 years). The mean HSDI score was 42.7 (SD 9.3). Participants who ate meat were less likely to eat vegetables (p < 0.001) and those who ate non-animal protein foods were more likely to eat more fruit (p < 0.001), vegetables (p < 0.05), and milk, yoghurt and cheese (p < 0.05). After adjusting for age, sex and body mass index, multivariable regression found the strongest predictor of the likelihood of being in the lowest total HSDI score tertile were people who only took a bit of notice OR (95%CI) 5.276 (1.775, 15.681) p < 0.005 or did not pay much/any attention to the health aspects of their diet OR (95%CI) 8.308 (2.572, 26.836) p < 0.0001. HSDI provides a new reference standard to assess adherence to a healthy and sustainable diet.
The world-wide rise in obesity parallels growing concerns of global warming and depleting natural resources. These issues are often considered separately but there may be considerable benefit to ...raising awareness of the impact of dietary behaviours and practices on the food supply. Australians have diets inconsistent with recommendations, typically low in fruit and vegetables and high in energy-dense nutrient-poor foods and beverages (EDNP). These EDNP foods are often highly processed and packaged, negatively influencing both health and the environment. This paper describes a proposed dietary assessment method to measure healthy and sustainable dietary behaviours using 4-days of food and beverage images from the mobile food record (mFR) application. The mFR images will be assessed for serves of fruit and vegetables (including seasonality), dairy, eggs and red meat, poultry and fish, ultra-processed EDNP foods, individually packaged foods, and plate waste. A prediction model for a Healthy and Sustainable Diet Index will be developed and tested for validity and reliability. The use of the mFR to assess adherence to a healthy and sustainable diet is a novel and innovative approach to dietary assessment and will have application in population monitoring, guiding intervention development, educating consumers, health professionals and policy makers, and influencing dietary recommendations.
The assessment of dietary intake underpins population nutrition surveillance and nutritional epidemiology and is essential to inform effective public health policies and programs. Technological ...advances in dietary assessment that use images and automated methods have the potential to improve accuracy, respondent burden, and cost; however, they need to be evaluated to inform large-scale use.
The aim of this study is to compare the accuracy, acceptability, and cost-effectiveness of 3 technology-assisted 24-hour dietary recall (24HR) methods relative to observed intake across 3 meals.
Using a controlled feeding study design, 24HR data collected using 3 methods will be obtained for comparison with observed intake. A total of 150 healthy adults, aged 18 to 70 years, will be recruited and will complete web-based demographic and psychosocial questionnaires and cognitive tests. Participants will attend a university study center on 3 separate days to consume breakfast, lunch, and dinner, with unobtrusive documentation of the foods and beverages consumed and their amounts. Following each feeding day, participants will complete a 24HR process using 1 of 3 methods: the Automated Self-Administered Dietary Assessment Tool, Intake24, or the Image-Assisted mobile Food Record 24-Hour Recall. The sequence of the 3 methods will be randomized, with each participant exposed to each method approximately 1 week apart. Acceptability and the preferred 24HR method will be assessed using a questionnaire. Estimates of energy, nutrient, and food group intake and portion sizes from each 24HR method will be compared with the observed intake for each day. Linear mixed models will be used, with 24HR method and method order as fixed effects, to assess differences in the 24HR methods. Reporting bias will be assessed by examining the ratios of reported 24HR intake to observed intake. Food and beverage omission and intrusion rates will be calculated, and differences by 24HR method will be assessed using chi-square tests. Psychosocial, demographic, and cognitive factors associated with energy misestimation will be evaluated using chi-square tests and multivariable logistic regression. The financial costs, time costs, and cost-effectiveness of each 24HR method will be assessed and compared using repeated measures analysis of variance tests.
Participant recruitment commenced in March 2021 and is planned to be completed by the end of 2021.
This protocol outlines the methodology of a study that will evaluate the accuracy, acceptability, and cost-effectiveness of 3 technology-enabled dietary assessment methods. This will inform the selection of dietary assessment methods in future studies on nutrition surveillance and epidemiology.
Australian New Zealand Clinical Trials Registry ACTRN12621000209897; https://tinyurl.com/2p9fpf2s.
DERR1-10.2196/32891.
This study examined the frequency of inherited retinal diseases (IRDs) as the reason for blindness registrations over the last two decades and the demographic and clinical phenotypes of inherited ...retinal disease (IRD)-related registrations.
Retrospective, observational study of individuals registered with a state-wide blind and vision-impaired registry. Low-vision or blindness-only (≤20/200 or ≤20°) certificates issued to children (0-15 years), working-age (16-64 years) and older-age (65 and older) adults were assessed. Sex and age distributions were examined for the top 20 reasons for certification. Demographic and clinical features of specific phenotypes of IRDs listed in the registry were examined.
Amongst 11824 low-vision certificates issued between July 1995 and January 2017, 679 (5.7%) listed an IRD as the reason for registration. In individuals with blindness-only certification (N=4919), IRDs was the second most common diagnosis (8.3%), overtaking glaucoma (8.1%) and diabetic retinopathy (5.4%). IRD was the second most common reason for low-vision certification amongst children (11.6%) and the most common reason amongst working-age population (23.3%). The mean±SD age for IRD-related blindness-only certification was 46±20 years. The top three phenotypes of IRD-related low-vision certification were non-syndromic retinitis pigmentosa (54%), Stargardt disease (12%) and macular dystrophy (8%).
Our findings of IRDs as a common cause of blindness in all ages justify continued funding for providing low-vision services and developing treatments for these conditions.
Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for ...accuracy in reference to a measure of true intake are rarely undertaken in a single study population.
To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner.
In a controlled feeding study with a crossover design, 152 participants 55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5 were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR). When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models.
The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: −2.9, 6.3%) using Intake24, 1.3% (95% CI: −1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods.
Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance.
This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.
Background: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs. Objective: The aim of this study ...was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia. Method: A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection. Results: All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient’s length of stay, Charlson Index, operation length and intensive care unit stay were common predictors. Conclusion: Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance. Implications: We have successfully developed a “real-time” risk prediction model, where patient risk scores are calculated and reviewed daily.
Report the age-standardized annual incidence of blindness registration due to age-related macular degeneration (AMD) in Australia in patients aged 50 years and older. Frequencies of photodynamic ...therapy (PDT) and intravitreal therapy (IVT) were examined.
Retrospective observational study.
Registry of the Association for the Blind of Western Australia with best-corrected visual acuity worse than 20/200 in the better-seeing eye.
Registering as blind aged 50 years or over.
Annual age-standardized incidence of blindness over 3 time periods: 1996-2001 (pre-PDT), 2002-2007 (PDT era) and 2008-2016 (IVT era). The rates of PDT and IVT usage were assessed.
Age-standardized annual incidence of blindness rose during the PDT era, reaching 72.5 cases per 100,000 person-years in 2004. The incidence declined from 2007 onwards, reaching 8.2 cases per 100,000 person-years in 2016 (IVT era). The age at AMD blindness registration increased from 82.7 to 84.9 and 83.7 to 86.0 years from the PDT era to the IVT era in both male and females (P < 0.001) respectively. Over the same time period, PDT usage increased in 2002 and declined in 2006, whereas IVT usage increased from 2009 by 3745 per year.
The increase in new blindness registrations due to AMD coincided with public funding of verteporfin for PDT, whereas the subsequent decline occurred when bevacizumab was used off-label and ranibizumab and aflibercept were publicly funded. An understanding of the effect of retinal therapy on public health measures may inform improvements in the allocation of limited resources.
Report the age-standardized annual incidence of blindness registration due to age-related macular degeneration (AMD) in Australia in patients aged 50 years and older. Frequencies of photodynamic ...therapy (PDT) and intravitreal therapy (IVT) were examined.
Retrospective observational study.
Registry of the Association for the Blind of Western Australia with best-corrected visual acuity worse than 20/200 in the better-seeing eye.
Registering as blind aged 50 years or over.
Annual age-standardized incidence of blindness over 3 time periods: 1996-2001 (pre-PDT), 2002-2007 (PDT era) and 2008-2016 (IVT era). The rates of PDT and IVT usage were assessed.
Age-standardized annual incidence of blindness rose during the PDT era, reaching 72.5 cases per 100,000 person-years in 2004. The incidence declined from 2007 onwards, reaching 8.2 cases per 100,000 person-years in 2016 (IVT era). The age at AMD blindness registration increased from 82.7 to 84.9 and 83.7 to 86.0 years from the PDT era to the IVT era in both male and females ( P < 0.001) respectively. Over the same time period, PDT usage increased in 2002 and declined in 2006, whereas IVT usage increased from 2009 by 3745 per year.
The increase in new blindness registrations due to AMD coincided with public funding of verteporfin for PDT, whereas the subsequent decline occurred when bevacizumab was used off-label and ranibizumab and aflibercept were publicly funded. An understanding of the effect of retinal therapy on public health measures may inform improvements in the allocation of limited resources.
Excess weight is a major risk factor for chronic diseases. In Australia, over 60% of adults are overweight or obese. The overconsumption of energy-dense nutrient-poor (EDNP) foods and low physical ...activity (PA) levels are key factors contributing to population obesity. New cost-effective approaches to improve population diet and PA behaviors are needed.
This 1-year randomized controlled trial (6-month intervention and 6-month follow-up) aims to investigate whether a tailored intervention using mobile technology can improve diet and PA behaviors leading to weight loss in adults (aged 18-65 years) who are overweight or obese and recruited through a social marketing campaign (LiveLighter).
All eligible participants will provide data on demographics and lifestyle behaviors online at baseline, 6 months, and 12 months. Using two-stage randomization, participants will be allocated into one of three conditions (n=200 per group): tailored feedback delivered via email at seven time points, informed by objective dietary (mobile food record app) and activity (wearable activity monitor) assessment; active control receiving no tailored feedback, but undergoing the same objective assessments as tailored feedback; and online control receiving no tailored feedback or objective assessments. Primary outcome measures at 6 and 12 months are changes in body mass, EDNP food and beverage consumption, and daily moderate-to-vigorous PA (measured via accelerometry). Secondary outcomes include change in fruit and vegetable consumption, daily sedentary behaviors, and cost effectiveness.
Enrolment commenced in August 2017. Primary outcomes at 12 months will be available for analysis from September 2019.
Tailored email feedback provided to individuals may deliver a cost-effective strategy to overcome existing barriers to improving diet and PA. If found to be successful and cost effective, upscaling this intervention for inclusion in larger-scale interventions is highly feasible.
Australian New Zealand Clinical Trials Registry ACTRN12617000554369; https://www.anzctr.org.au /Trial/Registration/TrialReview.aspx?id=371325&isReview=true.
DERR1-10.2196/12782.
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