Background: Electronic health (eHealth) literacy is a growing area of research parallel to the ongoing development of eHealth interventions. There is, however, little and conflicting information regarding the factors that influence eHealth literacy, notably in chronic disease. We are similarly ill-informed about the relationship between eHealth and health literacy, 2 related yet distinct health-related literacies. Objective: The aim of our study was to investigate the demographic, socioeconomic, technology use, and health literacy predictors of eHealth literacy in a population with moderate-to-high cardiovascular risk. Methods: Demographic and socioeconomic data were collected from 453 participants of the CONNECT (Consumer Navigation of Electronic Cardiovascular Tools) study, which included age, gender, education, income, cardiovascular-related polypharmacy, private health care, main electronic device use, and time spent on the Internet. Participants also completed an eHealth Literacy Scale (eHEALS) and a Health Literacy Questionnaire (HLQ). Univariate analyses were performed to compare patient demographic and socioeconomic characteristics between the low (eHEALS<26) and high (eHEALS≥26) eHealth literacy groups. To then determine the predictors of low eHealth literacy, multiple-adjusted generalized estimating equation logistic regression model was used. This technique was also used to examine the correlation between eHealth literacy and health literacy for 4 predefined literacy themes: navigating resources, skills to use resources, usefulness for oneself, and critical evaluation. Results: The univariate analysis showed that patients with lower eHealth literacy were older (68 years vs 66 years, P=.01), had lower level of education (P=.007), and spent less time on the Internet (P<.001). However, multiple-adjusted generalized estimating equation logistic regression model demonstrated that only the time spent on the Internet (P=.01) was associated with the level of eHealth literacy. Regarding the comparison between the eHEALS items and HLQ scales, a positive linear relationship was found for the themes “usefulness for oneself” (P=.049) and “critical evaluation” (P=.01). Conclusions: This study shows the importance of evaluating patients’ familiarity with the Internet as reflected, in part, by the time spent on the Internet. It also shows the importance of specifically assessing eHealth literacy in conjunction with a health literacy assessment in order to assess patients’ navigational knowledge and skills using the Internet, specific to the use of eHealth applications.

The demand for convenience and the increasing role of digital technology in everyday life has fueled the use of online food delivery services (OFD’s), of which young people are the largest users globally. OFD’s are disrupting traditional food environments, yet research evaluating the public health implications of such services is lacking. We evaluated the characteristics and nutritional quality of popular food outlets on a market-leading platform (UberEATS^®) in a cross-sectional observational study conducted in two international cities: Sydney (Australia) and Auckland (New Zealand). A systematic search using publicly available population-level data was used to identify geographical areas with above-average concentrations (>30%) of young people (15–34-years). A standardized data extraction protocol was used to identify the ten most popular food outlets within each area. The nutritional quality of food outlets was assessed using the Food Environment Score (FES) (range: −10 ‘unhealthiest’ to 10 ‘healthiest’). Additionally, the most popular menu items from each food outlet were classified as discretionary or core foods/beverages according to the Australian Dietary Guidelines. The majority of popular food outlets were classified as ‘unhealthy’ (FES range −10 to −5; 73.5%, 789/1074) and were predominately takeaway franchise stores (59.6%, 470/789, e.g., McDonald’s^®). 85.9% of all popular menu items were discretionary (n = 4958/5769). This study highlights the pervasion and accessibility of discretionary foods on OFD’s. This study demonstrated that the most popular food outlets on the market-leading online food delivery service are unhealthy and popular menu items are mostly discretionary foods; facilitating the purchase of foods of poor nutritional quality. Consideration of OFD’s in public health nutrition strategies and policies in critical.

PURPOSE: Ongoing supportive care using electronic health (eHealth) interventions has the potential to provide remote support and improve health outcomes for patients with breast cancer. This study aimed to evaluate the effectiveness of eHealth interventions on patient-reported outcomes (quality of life [QOL], self-efficacy, and mental or physical health) for patients during and after breast cancer treatment and patient-reported experience measures (acceptability and engagement).METHODS: Systematic review with meta-analyses (random-effects model) of randomized controlled trials was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Nine databases were searched using a prespecified search strategy. Patient-directed eHealth interventions for adult patients during or after active breast cancer treatment measuring QOL, self-efficacy, and mental (depressive, anxiety, and distress symptoms) or physical (physical activity, nutrition, and fatigue) health outcomes were included. Data from eligible full-text articles were independently extracted by six observers.RESULTS: Thirty-two unique studies (4,790 patients) were included. All were health self-management interventions, and most were multicomponent (videos, forums, and electronic reminder systems) websites. Meta-analyses revealed a significant effect of eHealth interventions on QOL (standardized mean difference [SMD], 0.20 [95% CI, 0.03 to 0.36]), self-efficacy (SMD, 0.45 [95% CI, 0.24 to 0.65]), distress (SMD, –0.41 [95% CI,–0.63 to –0.20]), and fatigue (SMD, –0.37 [95% CI, –0.61 to –0.13]). Twenty-five studies (78.1%) measured patient-reported experience measures. Acceptability (n = 9) was high, with high ratings for satisfaction (range, 71%-100%), usefulness (range, 71%-95%), and ease-of-use (range, 73%-92%). Engagement (n = 25) decreased over time, but disease-focused information and interactive support were most engaging.CONCLUSION: eHealth interventions may provide an acceptable and effective strategy for improving QOL, distress, self-efficacy, and fatigue among patients with breast cancer.

Objectives To quantify contemporary differences in cardiovascular disease (CVD) risk factor assessment and management between women and men in Australian primary healthcare services. Methods Records of routinely attending patients were sampled from 60 Australian primary healthcare services in 2012 for the Treatment of Cardiovascular Risk using Electronic Decision Support study. Multivariable logistic regression models were used to compare the rate of CVD risk factor assessment and recommended medication prescriptions, by gender. Results Of 53 085 patients, 58% were female. Adjusting for demographic and clinical characteristics, women were less likely to have sufficient risk factors measured for CVD risk assessment (OR (95% CI): 0.88 (0.81 to 0.96)). Among 13 294 patients (47% women) in the CVD/high CVD risk subgroup, the adjusted odds of prescription of guideline-recommended medications were greater for women than men: 1.12 (1.01 to 1.23). However, there was heterogeneity by age (p <0.001), women in the CVD/high CVD risk subgroup aged 35–54 years were less likely to be prescribed the medications (0.63 (0.52 to 0.77)), and women in the CVD/high CVD risk subgroup aged ≥65 years were more likely to be prescribed the medications (1.34 (1.17 to 1.54)) than their male counterparts. Conclusions Women attending primary healthcare services in Australia were less likely than men to have risk factors measured and recorded such that absolute CVD risk can be assessed. For those with, or at high risk of, CVD, the prescription of appropriate preventive medications was more frequent in older women, but less frequent in younger women, compared with their male counterparts. Trial registration number 12611000478910, Pre-results.

A healthy diet is an important component of secondary prevention of coronary heart disease (CHD). The TEXT ME study was a randomised clinical trial of people with CHD that were randomised into standard care or a text-message programme in addition to standard care. This analysis aimed to: 1) assess the effects of the intervention onadherence to the dietary guideline recommendations; 2) assess the consistency of effect across sub-groups; and 3) assess whether adherence to the dietary guideline recommendations mediated the improvements in objective clinical outcomes. Dietary data were collected using a self-report questionnaire to evaluate adherence to eight dietary guideline recommendations in Australia, including consumption of vegetables, fruits, fish, type of fat used for cooking and in spreads, takeaway food, salt and standard alcohol drinks. The primary outcome of this analysis was the proportion of patients adhering to ≥ 4 dietary guideline recommendations concomitantly and each recommendation was assessed individually as secondary outcomes. Data were analysed using log-binomial regression for categorical variables and analysis of covariance for continuous variables. Among 710 patients, 54% were adhering to ≥ 4 dietary guideline recommendations (intervention 53% vs control 56%, p = 0.376) at baseline. At six months, the intervention group had a significantly higher proportion of patients adhering to ≥ 4 recommendations (314, 93%) compared to the control group (264, 75%, RR 1.23, 95% CI 1.15–1.31, p < 0.001). In addition, the intervention patients reported consuming higher amounts of vegetables, fruits, and fish per week; less takeaway foods per week; and greater salt intake control. The intervention had a similar effect in all sub-groups tested. There were significant mediational effects of the increase in adherence to the recommendations for the association between the intervention and LDL-cholesterol (p < 0.001) and body mass index (BMI) at six months follow-up (p = 0.005). A lifestyle-focused text-message programme improved adherence to the dietary guideline recommendations, and specifically improved self-reported consumption of vegetables, fruits, fish, takeaway foods and salt intake. Importantly, these improvements partially mediated improvements in LDL-cholesterol and BMI. This simple and scalable text-messaging intervention could be used as a strategy to improve diet in people with CHD. Australia and New Zealand Clinical Trials Registry ACTRN12611000161921 . Registered on 10 February 2011.