Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium

The use of internet-based smartphone apps consistently improved consumers' healthy eating behaviors: a systematic review of randomized controlled trials

Introduction

Digital tools, such as mobile apps and the Internet, are being increasingly used to promote healthy eating habits. However, there has been inconsistent reporting on the effectiveness of smartphones and web-based apps in influencing dietary behaviors. Moreover, previous reviews have been limited in scope, either by focusing on a specific population group or by being outdated. Therefore, the purpose of this review is to investigate the impacts of smartphone- and web-based dietary interventions on promoting healthy eating behaviors worldwide.

Methods

A systematic literature search of randomized controlled trials was conducted using databases such as Google Scholar, PubMed, Global Health, Informit, Web of Science, and CINAHL (EBSCO). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to prepare the entire document. EndNote (version 20) was used for reference management. The risk of bias in the articles was assessed using the "Revised Cochrane Risk of Bias tool for randomized trials (RoB 2.0)" by the Cochrane Collaboration. Narrative synthesis, using text and tables, was used to present the results. The study was registered in PROSPERO under protocol number CRD42023464315.

Results

This review analyzed a total of 39 articles, which consisted of 25 smartphone-based apps and 14 web-based apps. The studies involved a total of 14,966 participants. Out of the 25 studies, 13 (52%) showed that offline-capable smartphone apps are successful in promoting healthier eating habits. The impact of smartphone apps on healthy adults has been inconsistently reported. However, studies have shown their effectiveness in chronically ill patients. Likewise, internet-based mobile apps, such as social media or nutrition-specific apps, have been found to effectively promote healthy eating behaviors. These findings were consistent across 14 studies, which included healthy adults, overweight or obese adults, chronically ill patients, and pregnant mothers.

Conclusion

Overall, the findings suggest that smartphone apps contribute to improving healthy eating behaviors. Both nutrition-specific and social media-based mobile apps consistently prove effective in promoting long-term healthy eating habits. Therefore, policymakers in the food system should consider harnessing the potential of internet-based mobile apps and social media platforms to foster sustainable healthy eating behaviors.

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions.