The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report

The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. Mobile technology might enable increased access to effective prevention of CVDs. Given the high penetration of smartphones into groups with low socioeconomic status, health-related mobile applications might provide an opportunity to overcome traditional barriers to cardiac rehabilitation access. The huge increase in low-cost health-related apps that are not regulated by health-care policy makers raises three important areas of interest. Are apps developed according to evidenced-based guidelines or on any evidence at all? Is there any evidence that apps are of benefit to people with CVD? What are the components of apps that are likely to facilitate changes in behaviour and enable individuals to adhere to medical advice? In this Review, we assess the current literature and content of existing apps that target patients with CVD risk factors and that can facilitate behaviour change. We present an overview of the current literature on mobile technology as it relates to prevention and management of CVD. We also evaluate how apps can be used throughout all age groups with different CVD prevention needs.

The mobile revolution--using smartphone apps to prevent cardiovascular disease.

Mobile health (mHealth) has been defined as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices and personal digital assistants. Cardiovascular mHealth is, arguably, leading the mHealth space, through innovation, research and implementation, and especially in the areas of prevention, cardiac rehabilitation and education. mHealth includes simple strategies, such as the use of short message service (SMS) or text messages in successful short-term smoking-cessation, weight loss and diabetes management programs. The recent Australian Tobacco, Exercise and Diet Messages (TEXT ME) randomised clinical trial addressed multiple cardiovascular risk factors. mHealth can also involve more complex strategies, such as smart phone applications (apps), global positioning systems (GPS) and Bluetooth technologies. Although many apps could be considered suitable for primary prevention, they are largely unregulated and most are not evidence-based. Some have been well-developed, such as the Food Switch app and an iPhone electrocardiogram (ECG) system. The "explosion" of apps has driven initiatives such as the Mobile Applications Rating Scale (MARS). More recently, the use of sensors to monitor and provide feedback to patients and healthcare providers is being explored. With almost two billion people currently owning a Smartphone, and 50% of adults (globally) predicted to own one by 2018, mHealth provides the prospect of delivering efficient, affordable healthcare services to widespread populations both locally and globally. In particular, it has the potential to reduce socioeconomic disparity and alleviate the burden of cardiovascular disease. There is now a need to rethink traditional health service structures and bioengineering capacity, to ensure mHealth systems are also safe, secure and robust.

mHealth in Cardiovascular Health Care

Despite tremendous growth in the consumption of gluten-free (GF) foods, there is a lack of evaluation of their nutritional profile and how they compare with non-GF foods. The present study evaluated the nutritional quality of GF and non-GF foods in core food groups, and a wide range of discretionary products in Australian supermarkets. Nutritional information on the Nutrition Information Panel was systematically obtained from all packaged foods at four large supermarkets in Sydney, Australia in 2013. Food products were classified as GF if a GF declaration appeared anywhere on the product packaging, or non-GF if they contained gluten, wheat, rye, triticale, barley, oats or spelt. The primary outcome was the 'Health Star Rating' (HSR: lowest score 0.5; optimal score 5), a nutrient profiling scheme endorsed by the Australian Government. Differences in the content of individual nutrients were explored in secondary analyses. A total of 3213 food products across ten food categories were included. On average, GF plain dry pasta scored nearly 0.5 stars less (P< 0.001) compared with non-GF products; however, there were no significant differences in the mean HSR for breads or ready-to-eat breakfast cereals (P≥ 0.42 for both). Relative to non-GF foods, GF products had consistently lower average protein content across all the three core food groups, in particular for pasta and breads (52 and 32% less, P< 0.001 for both). A substantial proportion of foods in discretionary categories carried GF labels (e.g., 87% of processed meats), and the average HSR of GF discretionary foods were not systematically superior to those of non-GF products. The consumption of GF products is unlikely to confer health benefits, unless there is clear evidence of gluten intolerance.

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Are gluten-free foods healthier than non-gluten-free foods? An evaluation of supermarket products in Australia

Background: Front-of-pack nutrition labeling (FoPL) schemes can help consumers understand the nutritional content of foods and may aid healthier food choices. However, most packaged foods in Australia carry no easily interpretable FoPL, and no standard FoPL system has yet been mandated. About two thirds of Australians now own a smartphone. Objective: We sought to develop a mobile phone app that would provide consumers with easy-to-understand nutrition information and support the selection of healthier choices when shopping for food. Methods: An existing branded food database including 17,000 Australian packaged foods underpinned the project. An iterative process of development, review, and testing was undertaken to define a user interface that could deliver nutritional information. A parallel process identified the best approach to rank foods based on nutritional content, so that healthier alternative products could be recommended. Results: Barcode scanning technology was identified as the optimal mechanism for interaction of the mobile phone with the food database. Traffic light labels were chosen as the preferred format for presenting nutritional information, and the Food Standards Australia New Zealand nutrient profiling method as the best strategy for identifying healthier products. The resulting FoodSwitch mobile phone app was launched in Australia in January 2012 and was downloaded by about 400,000 users in the first 18 months. FoodSwitch has maintained a 4-plus star rating, and more than 2000 users have provided feedback about the functionality. Nutritional information for more than 30,000 additional products has been obtained from users through a crowdsourcing function integrated within the app. Conclusions: FoodSwitch has empowered Australian consumers seeking to make better food choices. In parallel, the huge volume of crowdsourced data has provided a novel means for low-cost, real-time tracking of the nutritional composition of Australian foods. There appears to be significant opportunity for this approach in many other countries. [JMIR Mhealth Uhealth 2014;2(3):e37]

Chester Goodsell

Papers

FoodSwitch: A Mobile Phone App to Enable Consumers to Make Healthier Food Choices and Crowdsourcing of National Food Composition Data